6G 网络 AI 概念术语白皮书 6G Network AI Concept and Terminology 6GANA TG1 2022-1-19 摘要 本白皮书介绍网络 AI 相关概念术语定义，并从需求角度，进一步分析 6G 网络和计算、数据、AI 融合的不同选项，及网络 AI 潜在带来的如 AIaaS 这样的新服务。通过澄清网络 AI基础概念，定义术语及潜在的各类融合选项，支撑 6GANA 各工作组更高效的讨论网络 AI场景需求、架构、算法、管控系统等，加速共识的达成。网络 AI 概念术语白皮书 Network AI-Concept and terminology 摘要.1 1.背景.5 2.概念术语定义.5 2.1 网络 AI 概念术语定义.5 2.2 网络 AI 的 QoS：QoAIS.7 2.3 模型、算法、知识.8 2.4 相关概念澄清.11 2.4.1 网络智能化.11 2.4.2 内生 AI.11 3.网络 AI 分级分类定义.13 3.1 网络 AI 分级定义.13 3.2 S0：AI4NET 类别.15 3.3 S1：连接 4AI 类别.17 3.4 S2：算力融合类别.19 3.5 S3：数据服务类别.22 3.6 S4：算法融合类别.23 3.7 S5：编管服务类别.26 4.总结.29 参考文档.29 缩略语.30 附录.30 文档作者列表：贡献者 单位 彭程晖，刘哲，王飞，王君 华为 邓娟，李刚，孙军帅 移动 李文璟，喻鹏，丰雷，周凡钦 北邮 杨立，谢峰，康红辉 中兴 王达，边森 亚信科技 段小嫣，艾明 大唐 杨旸，马牧雷，巩宸宇 上科大 张凯宾，温海波，陈端，顾方方 Nokia 上海贝尔 袁雁南，崇卫微 vivo 尤心，陈景然 OPPO 杨婷婷，宁嘉鸿 大连海事大学 夏旭，李鹏宇，王恒 电信 黄兵明 联通 温福喜 清华 肖泳 华科大 梁承超 重邮 阮磊峰 Intel 张海君 北科大 冯钢，秦爽，刘怡静 电子科大 1.背景 6G 将催化 AI 革命：以深度学习为代表的人工智能技术将走向成熟，而 6G 将成为普惠智能服务的使能器。从以往移动通信断代的历史看，一般新的一代移动通信系统的出现，对应会出现一些典型的新业务场景；对应 6G 的新业务场景很可能是源于通信与 AI 的共同设计带来的，其中涉及 CT、IT、数据、行业等跨不同领域的深度融合，使得 6G 成为一种新型的基础设施，来满足未来各 2B 行业从数字化走向智能化的行业发展趋势，以及未来 2C如元宇宙、触觉互联网等新应用走向更极致性能、更智能化和个性化。网络 AI 是基于这样的趋势出发提出的。目前业界对网络AI大的方向和趋势有一定的共识，即连接、算力、数据和 AI 在一定程度的融合创新。但对于网络 AI 具体的内涵、网络与 AI 相关的各个要素的融合程度等方面还没有形成统一的理解，相关的概念术语也没有被清晰、明确的定义出来。本白皮书正是从当前的现状触发，来定义网络 AI 相关的概念术语，并从需求角度进一步分析 6G 网络和计算、数据、AI 融合的不同选项，及网络 AI 潜在带来的如 AIaaS 这样的新服务，牵引业界相关的讨论更有效率，为业界加速相关共识的达成做出贡献。2.概念术语定义 2.1 网络 AI 概念术语定义 6G 的重要愿景之一是实现智能普惠和连接智能，因此，6G 除了作为连接基础设施之外，还应该从架构层基于原生设计支持 AI，例如结合 AI 应用在连接、算力、数据、算法等层面的要求，进行深度融合的优化设计，这个被认为是 6G 架构层面变革的主要驱动力之一。由此，该方向的研究引发了诸多的讨论，并引出了一系列的基础概念和术语，下面整理了主要的相关概念术语定义，澄清其主要内涵：AI for Network（AI4NET）：通过 AI 提升网络自身的性能、效率和用户服务体验。AI4NET 主要研究包括利用 AI 优化传统算法（如空口信道编码、调制）、优化网络功能（如移动性优化、会话管理优化）、优化网络运维管理（如资源管理优化、规划管理优化）等。Network for AI（NET4AI）：通过网络为 AI 提供多种支撑能力，使得 AI 训练/推理可以实现的更有效率、更实时，或者提升数据安全隐私保护等。NET4AI 将传统网络范围从连接服务，扩展到算力、数据、算法等层面。AI as a Service（AIaaS）：在网络基础设施中构建 AI 应用的服务能力，AI 应用包括网络自用的 AI 或者 AI 新业务，部署 AI 应用可以是运营商或第三方。Cloud AI：AI 在云上执行，AI 和网络架构是解耦的，只是利用底层网络将 AI 所需要的数据信息传递到云端，而云端是数据处理、训练和推理的主要智能中心。网络 AI：在网络中提供完整的分布式1AI 环境，包括 AI 基础设施、AI 工作流逻辑、数据和模型服务等。网络 AI 从概念上包含 AI4NET、NET4AI 以及 AIaaS。NET4AI 将支撑网络自用的 AI、AI新业务和 AIaaS 的业务能力。网络 AI 可独立于 Cloud AI 发展，也可互为补充。关于网络 AI 和 AI4NET/NET4AI 的关系 从目前普遍上的理解，AI4NET 中的 AI 是指用于网络自身的优化工具，例如通过 AI 增强网络的性能、优化网络运维效率等；而 NET4AI 中的 AI 是指通过网络承载的 AI 应用业务，例如机器视觉场景等。对于网络 AI 来说，以上的 2 种不同类型的 AI，都需要提供支持，即对于网络 AI 来说，AI4NET 和 NET4AI 是不同的场景，都需要从网络 AI 架构层面，提供各类的优化支持能力。网络 AI 除了支持 AI4NET 和 NET4AI 场景之外，还需要考虑 AI 能力的运营，即 AIaaS 这样的服务能力。未来网络通过提供网络 AI 的原生支持，自然需要扩展传统的通信生态体系，引入一个多方协作的生态系统，在商业和技术合作方面做到更简单、开放、灵活和可信等。1 分布式是对网络 AI 整体内涵描述，即终端、网元等都具备一定的网络 AI 相关能力，但不意味着网络中运行的每个具体的 AI 应用都是分布式的；对于是否增加“分布式”，没有完全达成一致。2.2 网络 AI 的 QOS：QOAIS 与传统通信网络的 QoS 主要考虑连接相关的性能指标不同，QoAIS 需要从连接、算力、算法、数据等多个综合维度来考虑评估网络 AI 的服务质量，因此广义上的 QoAIS 指标框架体系，将包括性能、开销、安全隐私和自治等。因此 QoAIS 首先从内容需要进行扩展，除了连接的服务质量外，还将包括：1）算力相关：基于 AI 模型训练和推理，数据预处理等算力功耗开销、效率等 2）算法相关：模型性能指标界、训练/推理耗时、泛化性、可重用性、鲁棒性、可解释性等 3）数据相关：样本空间平衡性、完整性、分布动态性、准确性、数据准备耗时等 AI 服务与 QoAIS 有一对一的对应关系。从类型上，AI 服务可以包括 AI 数据类、AI 训练类、AI 推理类和 AI 验证类。对每一类 AI 服务，均可以从性能、开销、安全、隐私、自治等多个维度设计评价指标，而每个维度又可以进一步展开设计，下图为一个示例说明：QoAIS 是网络 AI 编排管理系统和控制功能的重要输入，网络 AI 管理编排系统需要对顶层的 QoAIS 进行分解，再映射到对数据、算法、算力、连接等各方面。另外，QoAIS 也可以包括 AI 应用的业务体验指标，以信道压缩为例，可以选择归一化均方误差（Normalized mean square error,NMSE）或是余弦相似度作为信道恢复精度的KPI，也可以选择链路级/系统级指标（如误比特率或吞吐量等）作为反映信道反馈精度对系统性能影响的 KPI。此外，QoAIS 还可以包括 AI 服务的可获得性、AI 服务的响应时间（从用户发起请求到 AI 服务的首条响应消息）等与 AI 服务类型无关的通用的评价指标。2.3 模型、算法、知识 知识 Knowledge 知识是人类在实践中认识客观世界（包括人类自身）的成果，是数据和信息加工提炼后的结晶。与数据和信息相比，数据是对客观事物记录下来的可以鉴别的符号（包括数字、字符、文字、声音、图形、视频等），它提供了对客观事物的表示，但不提供判断或解释，数据是形成信息的重要原料；信息是对客观世界各种事物的特征的反映；知识是由信息形成的，对信息进行加工、抽象、分析、提炼和总结形成了知识，知识能够反映事物的本质。数据、信息和知识是对客观事物感知和认识的三个阶段。模型 Model 模型是为了某种目的，用字母、数字及其它数学符号建立起来的等式或不等式以及图表、图像、框图等描述客观事物的特征及其内在联系的数学结构表达式。最优化问题模型用包含变量的目标函数与约束条件来表示，求解最优化问题即为求目标函数的极值，以及求取得极值时变量的取值。机器学习模型的输入是样本数据，输出是期望的结果，同时也设定目标函数（一般是让模型的错误率尽量小）和约束条件，机器学习模型中有大量可以调节的参数，这些参数通过训练得到，从而学习到输入数据和输出结果之间人类无法直接理解的复杂关系。算法 Algorithm 广义的算法指完成某项工作的方法和步骤；数学中的算法指按照一定规则解决一类问题的明确和有限的步骤；计算机中的算法指用计算机来解决一类问题的方法和步骤。算法具有确定性、逻辑性、有穷性、正确性、顺序性和普遍性等特征。算法是求解模型的路径或方法，以机器学习为例，机器学习模型中有大量参数是未知的，通过算法可以训练出模型中的参数，从而得到一个最优或局部最优的机器学习模型，然后可用该机器学习模型对新的输入样本进行计算得到相应的输出结果。算力 Computing Force/Computing Power 算力指对数据的处理能力。本文中的算力指网络中的节点通过对数据处理实现特定结果的能力，包括计算能力和存储能力。智能 Intelligence 智能是“智力”和“能力”的总称。从感觉到记忆到思维这一过程，称为“智力”，智力的结果就产生了行为和语言，将行为和语言的表达过程称为“能力”，两者合称“智能”，即感觉、记忆、回忆、思维、语言、行为的整个过程被称为智能过程，它是智力和能力的表现。广义的智能指有效实现目标所必需的知识与技能，包括自然智能（生物智能）和人工智能。智能化 Intelligentize 智能化指事物在现代信息通信技术、大数据、物联网和人工智能等技术的支持下，实现具备能满足人的各种需求的属性的过程。人工智能 Artificial Intelligence 人工智能指机器模仿人类利用知识完成一定行为的过程，是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的新的技术科学。（通常将人工智能分为弱人工智能和强人工智能，前者让机器具备观察和感知的能力，可以做到一定程度的理解和推理，而强人工智能让机器获得自适应能力，像人类一样可以思考，解决一些之前没有遇到过的问题。目前的研究都集中在弱人工智能方面。）利用知识的过程包括怎样表示知识、获得知识、传递知识、以及使用知识。一般认为，推动人工智能发展的三要素包括：数据、算法和算力，其中数据是基础（原材料），算法是途径（加工过程），算力是基础设施（动力）。在这一过程中，如何结合知识、利用知识还需要做更进一步的研究。机器学习 Machine Learning 研究怎样使用计算机模拟或实现人类学习行为的一门学科，即利用算法解析数据，不断学习，对世界中发生的事物做出判断和预测的一项技术（或利用计算机作为工具模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能）。机器学习的一般过程包括：收集数据、识别数据特征、建立模型、通过对数据进行训练形成有效的模型、使用模型对新数据进行分类/预测。机器学习是实现人工智能的重要手段，按照基本原理或流派分类，可分为符号主义学习、连接主义学习、统计学习和深度学习；按照学习方式分类，可分为有监督学习、无监督学习、半监督学习、强化学习、迁移学习、深度学习等。知识图谱 Knowledge Graph 知识图谱是知识域可视化或知识领域映射地图，是显示知识发展进程与结构关系的一系列不同的图形，用可视化技术描述知识资源及其载体，挖掘、分析、构建、绘制和显示知识及它们之间的相互联系。知识图谱的主要目标是描述真实世界中存在的各种实体和概念，以及他们之间的各种关系，从而构成一张巨大的语义网络图，图中节点表示实体或概念或值，边则由属性或关系构成。知识图谱属于人工智能的范畴，实现人工智能的思路很多，知识工程是其中之一。知识工程中，知识表示是非常重要的任务，要应用知识，就必须在计算机系统中合理地表示，而知识表示的一种重要方式就是知识图谱。相关概念之间的关系如图 1 所示。图 1：人工智能与知识图谱的关系示意图 集中式处理 Centralized Processing 集中式处理指将所有信息放到一个统一的信息中心进行处理。分布式处理 Distributed Processing 分布式处理指将不同地点的，或具有不同功能的，或拥有不同数据的多台计算机利用通信网络连接起来，让各个计算机各自承担同一个工作任务的不同部分，在控制中心的管理协调下同时运行，共同完成一个工作任务。分布式机器学习 Distributed Machine Learning 人工智能人工智能知识工程知识工程知识表示知识表示知识图谱知识图谱分布式机器学习指利用多个计算机节点（或称为工作者 worker）进行机器学习，以便提高性能、保护隐私、并可扩展至更大规模的训练数据和更大的模型。在大数据和大模型的双重挑战下，大规模机器学习（尤其是大规模深度学习）模型的训练对计算能力和存储容量都提出了新的要求：计算复杂度高，导致单机训练会消耗无法接受的时长，需要使用并行度更高的处理器或计算机集群来完成训练任务；存储容量大，导致单机无法满足需求，需要使用分布式存储，因此提出了分布式机器学习。分布式机器学习的关键任务包括：数据和模型的划分（数据与模型怎样分布到各个节点上）、单机机器学习模型的算法及优化（各个节点上的模型如何优化）、节点间通信（各节点之间如何传递消息）、以及模型的聚合（各个节点的模型如何聚合成最终模型）。根据对并行任务的不同分解，可分为：数据并行（data parallelism），模型并行（model parallelism），以及模型与数据混合并行（Hybrid parallelism）。典型的分布式机器学习框架有联邦学习、群体学习和集成学习。2.4 相关概念澄清 2.4.1 网络智能化 网络智能化是指将 AI 等智能化技术与通信网络的硬件、软件、系统、流程等深度融合，利用 AI 等技术助力通信网络运营流程智能化，提质、增效、降本；促进网络自身的技术和体系变革，使能业务敏捷创新，推动构建智慧内生网络。2.4.2 内生 AI 内生 AI 是指在架构层面通过内生设计模式来支持 AI，而不是叠加或外挂的设计模式。对于内生设计模式的驱动力，主要包括如下几个方面：1)网络高水平自治：目前网络自治水平不高，需要各类网络内生 AI 能力支持实现对运营商和用户意图的感知和实现，实现网络的自我优化、自我演进，最终实现网络的高水平自治。2)智能普惠：面向行业用户，助力千行百业的数智化转型，实现“随时随地”智能化能力的按需供应；相比云服务供应商，提供实时性更高、性能更优的智能化能力服务；另外提供行业间的联邦智能，实现跨域的智慧融合和共享。3)提供高价值的新型业务和极致业务体验:终端存在大量数据，终端的计算能力也越来越强，考虑到数据隐私需求，需要内生智能协同网络和终端，为 2C 客户提供极致业务体验和高价值新型业务。4)网络安全可信：未来网络将承载更多样化的业务，服务更多的应用场景，承载更多类型的数据，因此网络将面临大量新的复杂的攻击方式。基于 AI 的安全能力在 6G 网络的各环节嵌入，实现自主检测威胁、自主防御或协助防御等。从以上驱动力分析可以看出，6G 网络除了满足基本的通信连接需求之外，还需要考虑计算、数据、模型/算法等多个方面，即 6G 需要通过架构层面的内生 AI 设计，来满足网络AI 多样化的新业务场景和网络自治优化等需求，包括应用于网络自身优化和用户体验的AI（如用 AI 重写的空口），也包括第三方的 AI 新业务。内生 AI 的内涵：6G 网络内生 AI 为网络高水平自治，行业用户智能普惠，用户极致业务体验，网络内生安全等提供所需的实时、高效的智能化服务和能力。是在 6G 网络架构内部提供数据采集，数据预处理，模型训练，模型推理，模型评估等 AI 工作流全生命周期的完整运行环境，将 AI 服务所需的算力、数据、算法、连接与网络功能、协议和流程进行深度融合设计。外挂 AI 模式：基于外挂设计的 AI 应用特征，一般是采用打补丁等方式进行，存在如下几个方面的挑战：缺乏统一的标准框架，导致 AI 应用缺乏有效的验证和保障手段，AI 应用效果的验证是在事后进行，这样端到端的整体流程长并且很复杂，中间过程一般需要大量的人力介入，对现网的影响也比较大，这导致了目前 AI 很难真正应用到现网中。外挂模式难以实现预验证、在线评估和全自动闭环优化。在外挂模式下，AI 模型训练通常需要预先准备大量的数据，而现网集中采集数据困难，传输开销也大，导致 AI模型迭代周期较长，训练开销较大、收敛慢、模型泛化性差等问题。外挂模式下，算力、数据、模型和通信连接属于不同技术体系，对于跨技术域的协同，只能通过管理面拉通进行，通常导致秒级甚至分钟级的时延，服务质量也难以得到有效保障。由于外挂 AI 模式存在上述诸多问题和挑战，因此 6G 网络采用内生 AI 方式设计已成为业界普遍共识。3.网络 AI 分级分类定义 3.1 网络 AI 分级定义 对于网络 AI 的研究，实质是研究网络和 AI 相互融合的关系，因此有必要从一开始就先明确网络和 AI 相互关联有哪些选项和发展升级路径。下图主要是从网络视角，看网络与 AI融合不同分级，随着级别越高，本质是网络与 AI 相互融合的更紧密和更全面深入，也对6G 网络需要设计的领域范围提出个更多的要求。其中 S0 是 AI4NET，S1S4 是NET4AI，S5 是 AIaaS，如下图所示：S0-AI4NET：在这个分类下，AI 主要作为工具来优化网络，对原有的网络架构不一定产生影响，例如用基于数据的 AI 模型替换网络中的传统的数值算法来优化网络性能和用户体验，或在运维领域实现智能运维等。AI4NET 在 5G 已开展相关的研究和应用，到了 6G，随着深度学习为代表的 AI 技术走向成熟，融合连接 算力的新型基础设施的出现，相关的应用将更丰富和成熟，并可能进一步深化演进，获得更多增益，出现更多的场景，支撑网络自身的全智能化。S1-连接 4AI：从网络的基础连接服务出发，将 AI 作为一类特殊的业务，分析连接或组网方面的特殊的要求，例如对比 5G 已有的连接 QoS 服务，AI 新业务可能在可达性、计算速度、吞吐量、时延、可靠性、安全隐私等方面有新的诉求，需要研究 6G网络如何更好的满足这些新的诉求。S2-（连接 算力）4AI：6G 将成为连接 算力的新型基础设施，可以满足 AI 所需的连接和计算服务；并可能进一步基于 AI 的连接和算力融合控制需求，6G 架构内生支持网算一体化或云网端一体化等。S3-（连接 算力 数据）4AI：6G 网络将提供数据服务，可以满足 AI 所需的连接、算力和数据服务；并可能进一步基于 AI 对这些方面的融合控制需求，6G 架构内生支持网算数一体化，实现安全可信的广义数据服务。S4-（连接 算力 数据 算法）4AI：6G 网络除了可以满足 AI 所需的连接、计算和数据服务，还对 AI 模型本身有一定程度的感知，并基于对 AI 模型的不同层次的认知，6G 架构内生支持对具体的 AI 模型实施自适应的针对性优化和模型拓展新构建的范式。S5-AIaaS：提供网络 AI 相关的连接、算力、数据和 AI 工作流的编管，并支持在网络基础设施中构建 AI 应用的服务能力。3.2 S0：AI4NET 类别 类别 1：空口物理层 AI 指 AI/ML 在物理层模块中的应用，例如将 AI/ML 应用于信道建模和估计、信道编码、调制、MIMO 和波形设计。AI/ML 可以用来提取无线信道的时域、频域和空域特征，如通过神经网络学习无线信道的时间相关性，经过训练后的模型就可以用于提供更准确的信道信息。AI/ML 也可以直接用作译码器，一些研究表明神经网络译码器不仅可以降低复杂度，还可以更好的补偿非线性。AI/ML 在 MIMO 系统中也有着广泛的应用，例如将每个天线集和频段中的信道通过全连接网络映射到另一个天线集和频段中的信道，即在 FDD 系统中国可能可以通过上行信道探测直接获取下行信道信息；还可以通过 AI/ML 压缩信道状态信息（CSI），来降低 CSI 上报开销等等。因此，通过 AI/ML，可以说是为不同物理层功能提供了一种通用的优化模块，增加了物理层的适应性和灵活度。AI/ML 可以说是为进一步提升无线链路性能，挖掘潜在增益提供了新的路径。类别 2：空口高层 AI AI/ML 应用于空口高层多用户处理场景，包括功控、QoS 管理、资源分配、自适应调制编码（AMC）等方面。其中，资源分配是基站 MAC 层的一个重要功能，可分配的资源包括接入机会、传输机会、功率或频谱等，一些研究表明，通过 AI/ML可以优化资源分配算法，提升资源适用效率。传统的自适应调制编码大多是被动的，它们根据接收机的反馈来调整调制和编码方案，通过更广泛的学习方案选择经验，AI/ML 可优化 AMC 来做出更优的选择。这些应用本质是基于 AI/ML 开展自主和积累式的学习，来优化相关的调度算法，使得基站变得越来越聪明，可以记住经验教训来支持未来做出更正确的决策。这样的方式转换，可能在小区级控制调度上带来巨大的性能增益。因此，通过在无线空口物理层、MAC 以及相关的协议信令中大量应用 AI/ML 方法，最终可能出现通过 AI 重构未来无线通信的空口。类别 3：系统 AI AI/ML 应用于接入网 RAN/核心网 CN 系统架构的场景。在 RAN AI 应用场景中，通过在基站之间传递切换的回报（reward）信息，可以帮助各基站基于 AI/ML 的切换持续的学习和优化。在 CN 中，3GPP 定义了 NWDAF 来支持 AI/ML 所需的数据收集、处理和 AI/ML 模型应用部署，例如 AI/ML 可应用于合成网络切片，实现异构网络，如地面网络于非地面网络的一体化，通过 AI/ML 协调复杂的多层次异构网络，为用户提供最佳的覆盖。类别 4：运维 AI AI/ML 在网络管理运维系统中的应用。网络管理运维工作伴随着网络和业务的各个发展阶段，主要包括：规划、建设、维护、优化、运营 5 个主要环节，这些环节组成了网络管理全生命周期。运维 AI 是指利用 AI 技术，进行网络全生命周期的运维和管理，主要包括以下功能：设计编排功能：为了适应 6G 应用场景多样，业务需求多变的实际情况，通过智能感知，实现资源的自动化勘查，支持快速完成业务功能、网络能力、资源关联、调用接口等设计工作。同时基于设计结果自动化的完成业务、网络、资源的组合和生命周期流程编排。实现灵活的业务发放和网络资源调用。资源管理功能：通过实现通信网络资源数据管理、资源入网、调度、分配、核查、变更，端到端网络资源拓扑视图等应用，提供数据服务能力统一封装开放能力，例如利用图像识别等 AI 技术实现资源管理智能化，大幅降低人力成本。故障管理功能：利用 AI 能力实现网络集中监控，包括网络与业务端到端监控和故障闭环管理等应用，提供网络监控开放能力。性能管理功能：利用 AI 能力实现网络与业务质量的端到端分析，实现各类容量、质量、效率、效益主题分析应用。网络规划优化功能：基于网络数据中台，集中于无线网络规划与质量优化，结合强化学习等技术，实现多目标多参数联合优化。开放分析和优化能力，实现智能化的闭环优化。运维调度功能：实现运维人员和任务、网络割接、运维等统一集中调度管理，流程管理。可充分利用 RPA 等技术提供自动派单、知识推送等服务，通过统一流程引擎实现进度可视。3.3 S1：连接 4AI 类别 连接 4AI 主要分为如下 2 个方面：1)6G 网络如何为 AI 提供所需的定制连接服务，即：连接所承载的 AI 服务相关数据的类型；2)6G 网络如何为 AI 提供所需的组网服务。定制连接 按照连接所承载的 AI 服务相关数据的类型，可以分为以下类别：类别 1：用于传输 AI 相关的信令，例如：AI 分析信息请求/回应消息；AI 分析所需算力相关的请求/应答消息。这里，AI 相关信令可能的传输方式包括：a)作为用户面数据传输；b)与 NAS 信令相耦合/融合，例如：网络内生 AI 控制采用 NAS 信令，业务/应用相关 AI信令放在 container 中通过 NAS 信令透明传输，等。类别 2：用于传输 AI 输入数据。类别 3：用于传输 AI 模型。类别 4：用于传输 AI 分析信息，包括中间分析信息(当多实体进行联合分析时)。以上不同的类别对连接的 QoS、网络适应能力可能会提出新的要求。对于同一类别，例如类别 4，其中涉及的不用 AI 业务/应用对连接的 QoS、适应能力等也可能存在不同的要求。对于 AI 业务场景以及相关的连接性能需求，3GPP TS 22.261（基于 TR 22.874 的结论）针对 5G 系统也有了一些 KPI 需求层面的分析和结果，主要是吞吐量、时延、可靠性等传统连接性能指标的影响，即目前 3GPP 标准对 5G 系统如何支持不同的 AI 业务/应用从功能和性能角度进行了分析，但目前还不涉及 AI 内生网络的设计。定制组网 按照支持 6G 内生 AI 所需的网络连接的架构与形态，可以分为以下类别：类别 1：集中式 AI 连接组网。采用一个中心 AI 控制功能实体进行 AI 策略控制，包括 AI分析信息收集、决策、下发等。类别 2：分布式 AI 连接组网，例如：边缘 AI。类似于 MEC，网络边缘的一个或多个功能实体(例如终端、网络功能、应用功能等)由本地的 AI 控制功能实体进行控制和管理。类别 3：子网式 AI 连接组网，例如：多用户设备（UE）之间组成的子网 AI、虚拟网络（VN）内的 AI 连接。类别 4：以上连接类型的混合连接组网。以上不同的类别组网形态，可以满足不同的 AI 业务/应用或控制场景需求。3.4 S2：算力融合类别 未来的网络架构中，算力将遍布于包括中心云、边缘云、网络设备、甚至终端设备在内的各种基础设施。算力以及附着之上的人工智能算法或功能应用，不仅能服务于网络或者设备本身用以改善性能优化网络运维，而且还可能通过统一的接口向外开放、服务于上层应用。算力和网络需要相互感知，以达到网络资源、算力资源的最佳利用，同时为用户提供最佳的体验。算力网络融合可以有以下逐步演进的 3 大类：类别 1：网元算力 此类算力通常以专用算力资源的形式服务于移动通信网络的网元（如基站或核心网），该算力资源仅用于实现网络功能或网元本身的计算处理；通常可用资源有限，主要用以通信性能提升或者网络运维优化等等定制化的 AI 应用服务（即 AI for network），如无线资源管理、信道估计、波束成形等等。此类别的算力典型地由基于通用处理器或可编程器件构成的计算单元和存储单元组成，对于可通过相应接口呈现在运营商的网管平台，通过管理面接口可在指定算力单元上完成 AI 算法的加载、更新或销毁，实现管理面对算力和算法的可管、可控。由于此类算力相对边缘计算、云计算能力比较有限，因此无法实现大规模计算和训练要求的 AI 应用，较难服务于第三方应用。类别 2：分布式外挂算力 此类算力通常以分布式的 EC（边缘计算）/MEC（多边缘计算）形式存在；作为云计算的演进，将计算从集中式数据中心下沉到通信网络接入网边缘，更接近终端用户。外挂算力以通用处理器 CPU 为主，也可包含高性能处理器 GPU 以及可编程加速卡等，相对丰富的算力为网络自身优化以及对高计算量和时延要求严苛的行业应用提供了可能。外挂算力以分布式的方式在更靠近用户的网络边缘提供算力服务，便于在提供更低时延的同时，减少对网络资源的消耗，以更好地服务一些行业应用，比如视频加速、网络自动驾驶、AR/VR等等低延时高带宽的场景以及包括非实时的无线协议处理及网络优化等在内的网络应用。由于通信网络能力开放给网管平台，分布的外挂算力也呈现在网管平台；因此，AI 应用等服务署可以综合考虑网络的信息以及分布的算力资源，进行业务的优化部署、调整等。此类算力上的 AI 等业务部署是通过管理面实现的，动态性不强，无法实现网络和算力在控制面的统一，无法及时响应用户的移动以及网络的变化；网络连接和业务连接是相对独立的，属于叠加模型，因此在资源的使用上有时无法达到最优。类别 3：分布式网络内生算力 新型网络架构中，各网元不仅有控制和转发能力，还兼顾计算能力，除网元之外，网络中还部署了计算节点，这种算网一体模式产生的算力称为网络内生算力。在网络设计之初，把算力当作网络的一种基本元素。算力遍布于网络，即算力广泛分布于云、边、端、中间网元，算力融于网络。算力服务、连接服务、以及综合考虑算力和连接的服务，都作为网络对外能提供的基本服务。网络内生算力可以促进内生智能的发展和部署，可以更好地支持无处不在的具有感知、通信和计算能力的基站和终端，实现大规模智能分布式协同服务，同时最大化网络中通信与算力的效用，适配数据的分布性并保护数据的隐私性。在新型网络架构中，网元和计算单元的控制面拉通，可以弥补算力融合类别 2 中的不足，可以及时的响应移动和网络的变化。网络内生算力可以促进未来智能应用的产生和发展，例如：沉浸式云 XR、全息通信、感官互联、智慧交互、通信感知以及数字孪生等。3.5 S3：数据服务类别 数据是 6G 的核心生产要素之一，相比于以通信网络运营数据和用户签约数据为主的 5G网络数据，6G 数据的范围和类型将随着 6G 服务从通信扩展至感知、计算和 AI 服务等而更加丰富。数据服务是数据提供者和数据消费者之间的抽象功能，解耦数据消费者和物理数据提供者。特别是在多数据提供者或多数据消费者时，数据服务有助于维持数据的完整性，通过重用性提高数据服务效率。6G 数据服务旨在高效支持端到端的数据采集、传输、存储和共享，解决如何将数据方便、高效、安全地提供给网络内部功能或网络外部功能，在遵从隐私安全法律法规的前提下降低数据获取难度，提升数据服务效率和数据消费体验。根据数据服务潜在的功能范围，可将数据服务分为 5 个类别：类别 1：数据收集/分发，为数据生产者和消费者提供基础数据收集的发布和订阅机制，提升数据收集/分发效率。类别 2：数据安全隐私，借助安全和隐私保护技术为用户和网络按需提供高质量的可信数据服务，既保证用户和网络的隐私保护，又保证数据的安全不可篡改及可溯源性。类别 3：数据分析，叠加利用模型、算法、知识和算力等提供统计信息、预测信息、网络异常分析和优化建议等信息，提升网络内部功能和网络外部功能的数据消费体验。类别 4：数据预处理，对所收集的数据进行格式转换、去噪和特征提取等通用工具类预处理满足智能应用需求。类别 5：数据存储，存储和检索所收集的数据，以及为数据安全隐私、数据分析或数据预处理等数据服务相关处理功能提供存储支持。3.6 S4：算法融合类别 类别 1：输入输出和模型协作 输入为进行模型训练的样本数据集以及进行推理的特定任务数据，在采用协作方式的 AI操作下，各种 AI 操作的上一步输出结果也将作为下一步协作节点的输入。输出为 AI 模型训练到某一步的输出结果，包括各种协作方式产生的需要发送给下一个协作节点的中间结果以及最终的输出结果。如何定义 AI 模型的输入输出与 AI 模型的类型以及功能有很大关系，AI 赋能各种功能可以主要通过以下两大类输入来帮助其实现智能化的提升：对于决策类AI模型，需要重点定义两大类输入和输出：1）第一大类输入是通用预测，包括业务预测、位置预测、负载预测和用户行为预测等；每个具体功能都会使用一种或几种通用预测作为输入判决的重要依据；2）第二大类输入是个性化数据，应用在不同场景时会有不同的个性化输入参数；3）个性化输出是AI 模型分析后的输出结果，需要按需对每个具体的功能点定义个性化输出，以实现合理、快速、准确的决策。对于非决策类AI模型，其具体形式是通过AI 模型推理的方式将数据处理的部分或全部步骤进行替代，具体场景下，对于输入和输出：1）输入要求对限制因素和期望效果进行个性化定义，AI 模型可根据上述两个个性化定义执行最佳的数据处理；2）输出主要是处理后的数据，不需特殊考虑 AI 模型的协作方式包括联邦学习、多智能体学习、模型分割、迁移学习、群体学习等。6G 网络感知 AI 模型的输入输出以及协作方式，从而合理调整资源，满足相应的 AI 操作。例如在模型分割的协作方式下，终端将计算到某一层的中间结果发送给网络，网络可以感知中间结果以及该 AI 操作采用模型分割的协作方式，从而根据网络自身情况，网络和 UE 的通信情况，向终端或应用服务器推荐更合适的分割点，帮助模型分割方式高效执行。类别 2：模型超参 机器学习的模型超参数是指模型外部的配置，主要用于对模型进行优化和调整，一般需要在训练之前进行手动调整，主要的超参数包括学习率、Batch Size、优化算法、迭代次数、隐藏层数目、隐藏层神经元数目、激活函数的选择等。学习率（Learning Rate 或作 LR）是指在优化算法中更新网络权重的幅度大小。学习率可以是恒定的、逐渐降低的，基于动量的或者是自适应的。不同的优化算法决定不同的学习率。当学习率过大则可能导致模型不收敛，损失不断上下震荡；学习率过小则会导致模型收敛速度偏慢，需要更长的时间训练。选择一个好的学习率不仅可以加快模型的收敛，避免陷入局部最优，减少迭代的次数，同时可以提高模型的精度。批样本数量（Batch Size）也是非常重要的模型超参数之一，指的是每一次训练神经网络送入模型的样本数，Batch Size 的大小影响模型的优化程度和速度，同时也直接影响到内存资源的使用情况，Batch Size 过小可能会导致梯度变来变去，模型收敛较慢，Batch Size 过大可能会导致内存不够用或程序内核崩溃。超参数的设置对于模型性能有着直接影响，其重要性不言而喻。合适的超参数设置调整可以最大化模型性能，更科学地训练模型，从而能够提高资源利用率。基于 6G 网络和模型的融合，一方面可以对于模型的超参数进行预测，从而协助第三方 AI 确定模型训练的超参数，最大化模型的性能。进一步的，6G 网络通过资源开放和模型开放，协助第三方应用进行模型的训练，例如通过资源开放，提供充足的计算、存储、通信资源，帮助 OTT在 6G 网络进行模型的训练。或者通过模型开放，将预训练好的模型开放给 OTT，OTT 仅需进行微调，从而高效的支持 OTT 的模型训练。类别 3：模型 KPI 模型的 KPI 主要包括了模型本身的性能指标以及模型对于通信网络的需求：1.在机器学习中，性能指标是衡量一个模型好坏的关键，也是我们进行模型训练的最终目标，如准确率，精确率，召回率，敏感度等。a)准确率是指在分类中，使用测试集对模型进行分类，分类正确的记录个数占总记录个数的比例；b)精确率和召回率是两个评价指标，但是它们一般都是同时使用。精确率是指分类器分类正确的正样本的个数占该分类器所有分类为正样本个数的比例。召回率是指分类器分类正确的正样本个数占所有的正样本个数的比例。2.在移动通信系统中，移动设备(如智能手机、汽车、机器人)正越来越多地用 AI/ML 模型取代传统算法(如语音识别、图像识别、视频处理)以实现应用程序。为了满足AI/ML 的需求，6G 网络也需要满足相应的 KPI。模型的种类繁多，且 AI 操作方法也多样，不同的 AI 操作方法和不同的模型大小，对 KPI 有着不同的需求。比如在联邦学习架构下，6G 网络需要保证一组联邦学习节点的整体 QoS，避免组内节点由于通信和计算能力的差异导致迭代效率低。除此之外，还可以通过模型分割以及调整分割点来保障 KPI，比如可以调整终端和 OTT 服务器间需要转递的中间参数的大小，从而满足不同的 KPI 需求。总的来说，6G 系统至少可以支持以下三种 AI/ML 操作:-AI/ML 在多个节点之间进行拆分；-基于 6G 系统进行 AI/ML 模型/数据分发和共享；-基于 6G 系统的分布式/联邦学习；为了支持以上三种 AL/ML 操作，保障模型训练/推理的实时性，模型的传输，上传和下载对于通信网络的需求也是较高的。目前 3GPP TS22.261 中给出了相应的通信KPI 要求，包括推理功能、模型下载、终端和网络服务器/应用联邦学习。类别 4：模型结构 机器学习中最主要的是深度学习，深度学习涉及的神经网络模型结构主要有全连接神经网络(MLP)，卷积神经网络(CNN),循环神经网络(RNN)等。全连接神经网络相邻两层之间任意两个节点之间都有连接。全连接神经网络是最为普通的一种模型，由于连接数多，导致大量的模型参数，从而占用更多的内存和计算资源。卷积神经网络一般是由卷积层、汇聚层和全连接层交叉堆叠而成的前馈神经网络，使用反向传播算法进行训练。卷积神经网络有三个结构上的特性：局部连接，权重共享以及汇聚。这些特性使得卷积神经网络具有一定程度上的平移、缩放和旋转不变性。和前馈神经网络相比，卷积神经网络的参数更少。循环神经网络是一类具有短期记忆能力的神经网络。在循环神经网络中，神经元不但可以接受其它神经元的信息，也可以接受自身的信息，形成具有环路的网络结构。和前馈神经网络相比，循环神经网络更加符合生物神经网络的结构。循环神经网络已经被广泛应用在语音识别、语言模型以及自然语言生成等任务上。6G 网络感知 AI 应用所涉及的模型结构，被认为是网路对 AI 应用最深层次的原生支持。网络通过感知模型的结构，进而全面感知 AI 模型，实现对 AI 应用全面的支持，灵活的分配资源以及辅助模型的计算等。例如，网络感知模型采用的是全连接结构，因全连接网络具有大量的参数，需要网络提供更多的通信资源。如果采用的是卷积神经网络，则模型参数较少，需要的传输资源也相对较小。3.7 S5：编管服务类别 灵活的网络 AI 部署主要涉及相关的编管平台能力的构建，为网络 AI 按需提供连接、算力、数据、算法等多方面的服务，并支持网络 AI 业务的部署、测试、管理和运营的自动化等。面向网络 AI 任务，主要包括如下多种类型的编管服务：类别 1，连接编管：面向网络 AI 任务，连接编排的一个重要目标是自动执行基于 AI 服务的网络请求,并最大限度地减少交付应用程序或所需的人工干预，在满足 AI 服务 QoS 的情况下，最优化网络资源效率。连接编管将基于网络能力开放、软件定义网络等底层能力，对连接实施编管。要实现编管效率的优化，连接编管需要具备一定的网络感知能力,并可以借助 AI 算法进行编管，以保持最佳的网络性能。类别 2，算力编管：算力编管是针对网络 AI 需求，提供最佳的算力资源分配和网络连接方案，并实现整网异构资源最优化的解决方案。算力编管通过网络分发服务节点的算力、存储信息等，并需要感知网络相关信息（如路径、时延等）。为了服务 AI 内生网络，算力编管将面对边缘动态、异构、分布式的资源，需要解决：资源标识：通过统一的资源标识体系，来标识不同所有方、不同类型的计算、存储、网络等资源，以便于资源信息分发与关联。算力感知、算力建模及算力评估：面向全网泛在的算力资源，对各类算力资源的状态及分布进行评估、度量以及建模，以作为算力资源发现、交易、调度的依据。多方、异构资源整合：通过网络控制面将来自不同所有方的资源信息进行分发，并与网络资源信息相结合，形成统一的资源视图。轻量化：针对网络边缘动态复杂环境，需要通过轻量化资源技术，解决业务实时迁移的问题。类别 3，数据编管：数据分布式存储：基于边缘网络模型去中心化的特性，处于边缘端的 AI 模型大多采用分布式计算的方式进行任务训练和推理，其数据分布亦采用分部署存储的形式，在未来 6G网络中，力求高效，准确地将模型数据进行分布式存储。数据安全隐私：当下，数据被国家认定为继土地、劳动力、资本、技术之后的“第五生产要素，6G 时代，边缘产生的数据量将会是呈现数量级的增长。大量的个人数据将被使用于训练模型和提供服务，如何保障数据安全隐私，是一个十分重要的研究方向。类别 4，AI 工作流编管：在网络 AI 训练中，可能同时运行数百个任务和上千个节点，有必要针对这样复杂的 AI 服务进行多层次的分解，如分解为多个工作流、多任务等形式，减低复杂度。AI 工作流编管可以基于环境和服务来提供的精细化编管服务，具体包括特征提取，模型训练，模型切割，边缘模型部署，模型推理，模型量化压缩等。4.总结 网络 AI 蕴含的是连接、算力、算法、数据跨技术领域的融合创新，是网络架构层面的重大变革，6G 网络的范围将不局限于连接服务，还包括内生的计算、数据、AI 等服务，这些将使得通信网络走向全新的领域。在这样的新领域中，一些新的想法、概念、术语和服务等因着跨技术领域的碰撞产生出来，本白皮书尝试去总结其中的关键部分，基于当前业界的思考和共识，给出定义并阐述其内涵；并以此为基础，分析网络 AI 的分级分类定义，包括不同类别下的各类服务描述、潜在的业务场景和需求、关键的研究方向等，为业界加速网络 AI 相关的共识达成做出贡献。参考文档 6GANA，6G Network AI 论坛倡议书 6GANA，6GANA 白皮书 Huawei，6G,the Next Horizon:From Connected People and Things to Connected Intelligence IMT-2030(6G)Promotion Group,6G vision and candidate technologies ITU FG-NET-2030，“Network 2030:A blueprint of technology,applications and market drivers towards the year 2030 and beyond IMT-2020，5G 应用创新发展白皮书 Hexa-X，6G Vision,use cases and key societal values Yang,Y.Multi-tier computing networks for intelligent IoT.Nat Electron 2,45(2019).缩略语 3GPP 3rd Generation Partnership Project 6G ANA 6G Alliance of Network AI AI Artificiel Intelligence ML Machine Learning ICT Information and Communication Technology 附录 1.AI 相关概念定义 策略 Strategy/Policy 策略在不同的研究领域中有不同含义。广义的策略是指为了在不确定环境中实现长期目标或总体目标而确定的总体计划。策略描述了最终目标是如何通过一定的方法或资源实现的，它可以是专门制定的，也可以是抽象的组织或群体在行动中表现出来的行为模式。策略一词最早见于军事和管理领域，经济领域博弈论中将策略定义为博弈参与者在进行行动选择时会采用的规则。人工智能领域中不同方法流派对“策略”有不同的理解，其中基于统计学习的机器学习方法，包括监督学习和无监督学习，认为策略是确定机器学习模型的具体方法，如损失函数最小化、经验风险最小化等；而强化学习中的策略，一定程度上借鉴了博弈论中的概念，即强化学习中的智能体（agent）同环境（包括其他智能体）进行交互，并从中学到长期奖励最高的行为策略。方法 Method 基于统计学习的机器学习方法由模型、策略、算法三要素构成，即首先考虑要学习的是什么模型，接着需要考虑按照什么策略选择最优模型，最后再用特定的算法确定模型的参数值，实现模型的最优化。构建一种基于统计学习的机器学习方法就是确定具体的三要素。深度学习 Deep Learning 是机器学习领域的一个研究方向，通过建立模拟人脑思维过程进行学习的神经网络，来实现对事物的解释、分析和学习。深度学习一般使用包含复杂结构或由多重非线性变换构成的多个处理层对数据进行高层抽象从而实现任务（但这种抽象一般来说是不可解释的）。典型的深度学习模型包括：卷积神经网络模型（CNN），循环神经网络网络模型（RNN），深度置信网络模型（DBN），生成对抗网络模型（GAN），深度强化学习模型（RL）等。人工智能与机器学习之间的关系如图 2 所示。图 2：人工智能与机器学习之间的关系示意图注 注图中各机器学习方法之间没有绝对的界限，各方法分类角度不同，之间会有重叠。强化学习 Reinforcement Learning 强化学习是一个学习最优策略（policy），可以让智能体（agent）在特定环境（environment）中，根据当前状态（state），做出行动（action），从而获得最大回报（reward）的迭代过程。强化学习和有监督学习与无监督学习最大的不同是不需要大量的训练数据，而是通过自己不断的尝试来学会某些知识或技能。按照是否有模型，强化学习可分为有模型学习（Model-based）和免模型学习（Model-free）两类。人工智能机器学习 有监督学习迁移学习无监督学习半监督学习强化学习深度学习 迁移学习 Transfer Learning 指利用数据、任务或模型之间的相似性，将在源领域学习过的模型，应用于目标领域的一种学习过程。迁移学习的核心是找到源领域和目标领域之间的相似性。根据迁移场景的不同，迁移学习可分为归纳式迁移学习（Inductive TL）、直推式迁移学习（Transductive TL）和无监督迁移学习（Unsupervised TL）等。联邦学习 Federated Learning 又称为联邦机器学习（Federated Machine Learning），是一种分布式机器学习框架。即多个参与方通过协作训练得到一个共享的全局模型，在这个过程中各参与方无需分享本地数据，该框架能有效帮助多个机构在满足用户隐私保护、数据安全和政府法规的要求下，进行数据使用和机器学习建模。联邦学习可以避免非授权的数据扩散并解决数据孤岛问题。根据数据的分布特点，联邦学习分为横向联邦学习、纵向联邦学习与联邦迁移学习三类。横向联邦学习 Horizontal Federated Learning 又称为特征对齐的联邦学习（Feature-Aligned Federated Learning），其本质是样本的联合，即联邦学习参与方的训练样本重叠很少，但各样本的数据特征重叠很多。横向联邦学习的一般过程为：中心节点建立一个基本的全局模型，将全局模型的结构与参数告知参与方；参与方利用本地数据训练模型，并将训练好的模型参数（加密后）返回给中心节点；中心节点聚合各参与方返回的参数，整合形成更精准的全局模型再分发给各参与方。通过横向联邦学习可以增加训练样本总量。纵向联邦学习 Vertical Federated Learning 也称为样本对齐的联邦学习（Sample-Aligned Federated Learning），其本质是特征的联合，即纵向联邦学习参与方的训练样本重叠很多，但各样本的数据特征重叠很少。纵向联邦学习的一般过程为：首先对参与方数据进行加密样本对齐，获得重叠的样本数据；中心节点生成秘钥对，并向各参与方发送公钥用以加密需要传输的数据；参与方各自初始化和自己相关的模型参数，然后在本地对所选出的样本数据进行训练，分别训练出和自己相关的特征中间结果；各参与方将训练出的特征中间结果基于公钥进行加密（一般为同态加密）后进行交互；各参与方基于交互得到的加密中间结果继续进行训练，并将训练后的模型参数（依然是加密的）发送给中心节点；中心节点进行解密后分别将各自的模型参数再返回给各参与方；各参与方更新各自的模型参数。在整个过程中，各参与方都不知道另一方的数据和特征，且训练结束后参与方只得到和自己相关的模型参数，即半模型，因此在预测时，需要参与方之间协作完成。通过纵向联邦学习可以增加训练数据的特征维度。联邦迁移学习 Federated Transfer Learning 联邦迁移学习中各参与方之间的训练样本和数据特征的重叠都很少。联邦迁移学习的一般过程为：不同参与方根据不同来源的数据初始化并训练各自的模型；然后对训练出的中间结果进行（同态）加密，使之不能直接传输以免泄露参与方的隐私；参与方交互加密后的中间结果以协助对方进行训练，即双方对这些模型进行联合训练以得到最终的最优模型，再将最优模型返回给各参与方。上述过程与纵向联邦学习的过程类似，只是交互的中间结果不同。通过联邦迁移学习可克服数据样本少或标签不足的情况。群体学习 Swarm Learning 群体学习是联邦学习和区块链的融合，主要解决联邦学习在融合模型时，过于依赖中心节点，以及集中式融合海量本地节点模型时计算和通信开销大的问题。群体学习相比联邦学习的显著特点是，联邦学习是数据在本地而模型融合在云端（中心节点），群体学习是数据和模型融合都在本地节点，不需要中心节点。群体学习在联邦学习的基础上，引入区块链技术，利用去中心化的多节点分布式可信机制，实现 1）通过多节点分布式处理模型数据，减小融合模型时的高计算量；2）通过分布式可信免去对中心节点的依赖，也规避单一中心节点失效风险，提升了习得模型的可信度；3）区块链的防篡改、可追溯特性，可保护模型免受攻击。集成学习 Ensemble Learning 集成学习是一种分布式机器学习框架，通过构建多个学习器并将其结合起来完成学习任务。由于在实际应用中单一的学习器往往不能达到理想的学习效果，且有时单一学习器会导致过拟合，因此使用多个学习器进行集成学习往往能够达到更好的学习效果。根据学习器训练的模型是否为同类模型，集成学习分为同质集成学习和异质集成学习两类。a)若训练的多个模型是同一类型的模型，则为同质集成学习，所用算法称为“基学习算法”，每一个模型称为“基学习器”。b)若训练的多个模型非同一类型的模型，则为异质集成学习，每一个模型称为“组件学习器”或“个体学习器”。根据模型训练和结合策略的不同，集成学习可分为串行方法（Boosting）、并行方法（Bagging）和堆叠方法（Stacking）三类。Boosting Boosting 的工作机制是：首先基于初始训练集用初始权重训练出一个基学习器，再根据基学习器的表现更新训练样本的权重（如增大被误分样本的权重，减小被正确分类样本的权重），使得先前基学习器做错的样本在后续的训练过程中受到更多关注，然后基于调整权重后的训练集来训练下一个基学习器，如此重复，直到基学习器数目达到事先指定值 T，然后将这 T 个基学习器经结合策略进行整合，得到最终的学习器。Boosting 中的基学习器存在强依赖关系，必须串行执行。典型的 Boosting 算法有：AdaBoosting 算法、梯度提升决策树（Gradient Boosting Decision Tree：GBDT）算法、xgboost 算法等。Bagging Bagging 的工作机制是：首先从数据集中采用有放回的随机抽样来获取 T 个训练数据集，然后基于这 T 个数据集独立训练出 T 个基学习器，再将这 T 个基学习器经结合策略进行整合，得到最终的学习器。Bagging 中的基学习器不存在强依赖关系，可并行执行。并行执行的典型算法有Bagging 算法和随机森林（Random Forest）算法，随机森林的基本思想是构造多棵相互独立的决策树，形成一个森林，利用这些决策树共同决策输出类别。Stacking 集成学习中的结合策略是将不同基学习器进行整合的方法，一般采用的结合策略包括：对于分类任务使用简单的投票法（若分类预测时出现两个类票数一样时，则随机选择一个）；对于回归任务使用简单的平均法。还有一种结合策略是使用另一个机器学习算法将基学习器的结果结合在一起，这个方法就是 Stacking。Stacking 是一种组合多个模型的方法，其主要思想是：在进行模型结合时，不是对模型的结果进行简单的逻辑处理（如投票法或算术平均法），而是在模型外增加一层，形成两层模型。首先从初始数据集训练出初级学习器（第一层模型），然后将初级学习器的输出结果作为输入用于训练次级学习器（第二层模型），从而得到最终结果。各类分布式机器学习的关系如图 3 所示。图 3：各类分布式机器学习关系示意图 可解释人工智能 Explainable AI 可解释人工智能是一套流程和方法，可使人类用户能够理解和信任机器学习算法所产生的结果和输出，有助于描述人工智能模型的准确性、公平性、透明度和结果。关注可解释性的主要原因有两方面：1）当前以大数据与深度学习为基础的人工智能不可解释和不可理解，就事论事，泛化能力弱。当面对动态变化的环境、信息不完全、或存在干扰与虚假信息时，人工智能系统的性能会显著下降，这样的人工智能系统由于不可理解，无法实现人机交互，无法与人类协同工作。2）解决实际应用中人们对人工智能产品的信任问题，即人们需要知道 AI 给出答案的依据是什么，计算的过程是怎么样的，从而产生信任的依据，同时也促进组织采用负责任的方法进行 AI 开发。人工智能的可解释性可分为两个层面：1）解释：以人类可以理解的方式解释推理和决策的依据及过程；2）抗辩：针对人类的质疑能进行有效的抗辩。可解释人工智能具有两方面能力：1）自省和可解读能力：机器和人类可达成共同语言表达；2）自辨能力：机器能向人类解释其计算的机理与过程，从而产生有依据的可解释性。2.网络运维全生命周期管理的 5 个主要环节定义：分布式机器学习联邦学习横向联邦学习纵向联邦学习联邦迁移学习集成学习BoostingBaggingStacking群体学习 规划：支持规划目标建立、规划方案设计、规划仿真全流程。支持从网络整体表现、产品运营战略、业务使用体验提升等角度建立规划目标；通过连通规划目标和规划方案的能力（包括拉通环境数据、业务需求数据、资源数据的多维度分析能力），实现业务覆盖、容量、带宽等规划目标；通过仿真能力，实现规划目标的仿真验证。建设：支持建设项目的立项、设计、验收全流程。根据规划设计完成交付、配置、以建设目标为基准完成网络能力、可用性等具体指标的验收。除以上整体流程外，以上各环节也可以通过自动化工具实现能力提升，包括自动化交付配置、自动化验收测试和闭环调整。维护：对网络整体表现、产品运营表现、业务使用体验、资源健康度进行管理、监控、分析。通过被动的监控和处理，或者通过对故障告警和性能劣化的主动感知分析以及自动化的资源调整实现网络、业务的恢复。通过售前、售中、售后的端到端支撑能力，提供贯穿于运维各项生产环节的自动化运维感知和决策信息的流转能力。优化：支持根据规划部门、市场部门、服务部门、运维部门的需求建立优化目标、输出优化方案、执行优化流程。支持基于网络整体表现、业务使用体验、资源健康度等不同维度设定优化目标；通过优化方案设计能力输出常态化或专题类优化方案；通过优化分析工具执行优化方案。运营：支持市场部门设定的优化市场战略，支持产品设计、订单接收、流程分析以及业务在网络中的配置、激活、上线、扩缩容以及变更等全生命周期管理业务工作，同时也包含业务上线、变更带来的业务保障、端到端测试、质量监控、投诉预处理、客户服务、用户满意度保障等工作内容，保障网络资源对业务的诉求提供可靠的能力支撑。
2022-12-19
37页
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R E P O R TM U L E S O F T R E S E A R C HInsights from 1,000 IT leaders on people,processes,and technologyIT Leaders Pulse Report 2022:Financial Services and Insurance 2Contents About this reportForewordExecutive summary:Financial services and insurance sector030425101805Process:Bringing IT and business teams togetherFrom technology enabler to business leaderPeople:Enhancing the employee experience06Technology:Empowering the workforce and creating exceptional experiences38Measuring future IT success3Financial services and insurance providers face an increasing list of challenges as they strive to create best-in-class customer and employee experiences while tackling a rapid increase in cross-sector pollination.In doing this,these organizations must consider new ways of looking at technology that accelerates time-to-market,and augments goals of efficient servicing and proper management of operations,risk,and compliance.Already tasked with managing a plethora of networks,devices,and applications critical to business growth,IT teams have now been elevated to a more strategic role,charged with driving multiple digital transformation initiatives often with little increase in team capacity or resources.These technology challenges are not unique to the financial services and insurance sector,which means IT leaders are in high-demand.As a result,IT retention grows more challenging every year and can impact innovation if unaddressed.But IT teams dont need to throw in the towel leveraging the right technology strategy with easy-to-use tools that empower everyone can help mitigate these challenges and drive improved business results.Dhiren Tiwari,Partner,PwC SAY A BEST-OF-BREED APPROACH HAS LED TO IT COMPLEXITY.725%PLAN TO INCREASE USE OF LOW/NO CODE DEVELOPMENT TOOLS IN THE NEXT 12 MONTHS.CONFIRM THAT THE GREAT RESIGNATION HAS WORSENED THE IT SKILLS GAP,WITH THE LARGEST GAPS IN THREE AREAS:62%IT AND SOLUTION ARCHITECTURE.48%CLOUD/INFRASTRUCTURE MANAGEMENT.40%SOFTWARE DEVELOPMENT.93%SAY EXISTING IT PROCESSES ARE HINDERING EMPLOYEE EXPERIENCE.93%PLAN TO INVEST IN NEW TECHNOLOGY TO ADDRESS THE SKILLS GAP.61%ARE CREATING FUSION TEAMS BLENDING WORKERS WITH TECHNOLOGY,ANALYTICS,AND DOMAIN EXPERTISE WHO SHARE RESPONSIBILITY FOR BUSINESS AND TECHNOLOGY OUTCOMES.68U%CITE PEOPLE AND PROCESSES AS THE TOP IT INVESTMENT,AHEAD OF TECHNOLOGY(45%).THE SKILLS GAP INTENSIFIESTECHNOLOGY IS AN INVESTMENT PRIORITYCOMPLEXITY CONTINUESIT AND BUSINESS ARE ALIGNINGThe Great Resignation a recent phenomena where workers left or switched jobs in mass numbers as a result of shifting personal and professional priorities following the pandemic has evolved the role of senior IT leaders.Their focus has shifted to creating people-and experience-centric capabilities for customers and employees.The financial services sector is no exception.Financial services and insurance providers are under increased pressure from heightened customer expectations,advancements in new technology,and economic uncertainty.In turn,their IT teams are feeling the impact of greater demands and are looking to technology strategies and investments to help deliver customer and employee value faster.The findings of this report reveal the critical need for organizations within the financial services sector to reevaluate IT operating models,increase alignment to ease the pressure on IT teams,and empower everyone across the enterprise to innovate.MuleSofts IT Leaders Pulse Report,in partnership with Vanson Bourne,was produced from interviews with 1,000 senior IT leaders across the globe.Out of 1,000,149 were senior IT leaders from the financial services and insurance sector.Below are the statistics specific to this industry.Executive summary:Financial services and insurance sector 4IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCE5MuleSofts IT Leaders Pulse Report,in partnership with Vanson Bourne,was produced from interviews with 1,000 senior IT leaders across the globe.We conducted an online survey during June and July 2022 with participants from the United States,the United Kingdom,France,Germany,the Netherlands,Australia,Singapore,Hong Kong,and Japan.We used a rigorous,multi-level screening process to ensure that only suitable candidates participated in the survey.Respondents are all senior IT leaders,which are defined as those who hold a departmental or intermediate managerial position or above within the IT function.All respondents work at an enterprise organization in the public or private sector with at least 1,000 employees.About the reportFrom technology enabler to business leader S EC T I O N 01Experiences become increasingly importantAs digital transformation increasingly drives business strategy,senior IT leaders are moving from acting as IT operators to business leaders with deep technical expertise.Organizations are now realizing the importance of creating positive employee experiences to attract and retain talent after the Great Resignation.This type of exceptional experience is also expected for customer interactions.Today,86%of senior IT leaders agree that the experience an organization provides is as important as its products and services.86%OF SENIOR IT LEADERS AGREE THAT THE EXPERIENCE AN ORGANIZATION PROVIDES IS AS IMPORTANT AS ITS PRODUCTS AND SERVICES.01 From technology enabler to business leader7IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCESuccess relies on tech-enabled experiences Roughly four out of five of respondents agree that improved customer-facing and employee technologies are critical for their organization to compete.This means that senior IT leaders are now making important business decisions with technology-enabled experiences in mind.This includes everything from technology investments to workplace environments to team structures and opportunities.85%OF SENIOR IT LEADERS AGREE THAT IMPROVED CUSTOMER-FACING TECHNOLOGY IS CRITICAL FOR THEIR ORGANIZATION TO COMPETE.OF SENIOR IT LEADERS AGREE THAT IMPROVED EMPLOYEE TECHNOLOGY IS CRITICAL FOR THEIR ORGANIZATION TO COMPETE.01 From technology enabler to business leader8IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCE9IT LEADERS PULSE REPORT 2022:COMMUNICATIONS,MEDIA,AND TECHNOLOGYSenior IT leaders are shifting their investment priorities.Across all industries,IT investment priorities over the next 12 months are evenly split,with half going toward technology and the remainder being spent on people and processes.The remainder of this report highlights the ways organizations are using or plan to use automation,best-of-breed technologies,and low/no-code tools to empower their employees and create market-winning experiences.IT investing in technology,people,and processesTECHNOLOGYPROCESSESPEOPLE(EMPLOYEES)How organizations plan to prioritize IT investments over the next 12 months:50&$ From technology enabler to business leaderPeople:Enhancing the employee experienceC U S T O M E R S P O T L I G H Tits a new era for the world of work.as people recalibrate their lives,values,and priorities,many are leaving their jobs in search of better employment conditions and opportunities.accelerated by the pandemic,the great resignation has created new expectations for senior it leaders.Workers today want employers that offer work-life balance,remote-or hybrid-working policies,more meaningful work,flexible hours,or higher pay.and as companies lose talent,they experience short-term business disruptions and skills gaps that can become increasingly difficult to fill.to appeal to top talent in todays job market,companies must offer more than a competitive salary they must also consider employee wellbeing.many senior it leaders are already recognizing this and are urgently adjusting their operations to center their focus on people.02 PeoPle:enhancing the emPloyee exPerience11IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEWellbeing is shaping the future of workEmployee wellbeing now an investment priorityWhile technology remains a standout priority,nine out of ten senior IT leaders agree that investment in people is hugely important and theyre reevaluating their investment budgets accordingly.Over the next 12 months,the majority of respondents plan to invest in improving IT employee wellbeing,ahead of upskilling and increasing team headcount.This includes providing enhanced remote and flexible working capabilities.How organizations plan to invest in its IT employees over the next 12 months:87%INVESTING TO IMPROVE IT EMPLOYEE WELLBEINGUPSKILLING EXISTING IT EMPLOYEESINCREASING IT HEADCOUNT82xh%OF SENIOR IT LEADERS AGREE THAT INVESTING IN PEOPLE IS HUGELY IMPORTANT.02 PeoPle:enhancing the emPloyee exPerience12IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCESkills gaps related to implementation and management of new technologies is not a new issue for IT.However,the Great Resignation has made the problem much worse across a wide spectrum of IT functions.Most notably,three out of five(60%)senior IT leaders say they now have skills gaps within their IT and solutions architecture function,while almost half(45%)see gaps when it comes to cloud and infrastructure management.Where has the Great Resignation created skills gaps for IT:Skills gap stretch across IT functionsIT AND SOLUTION ARCHITECTURECLOUD/INFRASTRUCTURE MANAGEMENTSECURITY/INFOSEC60E98844)%3%SOFTWARE DEVELOPMENTDEVOPSNETWORK AND SYSTEMS INTEGRATIONDATABASE ADMINISTRATIONBUSINESS ANALYSIS/INTELLIGENCEPROJECT MANAGEMENTNO SKILLS GAPS HAVE BEEN CREATED DUE TO RECENT DISRUPTIONS IN THE LABOR MARKET02 PeoPle:enhancing the emPloyee exPerience13IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCE60XWSQ%Many senior IT leaders are turning to automation and self-serve initiatives to address the growing skills gap.Across industries,58%of organizations are tackling this by automating tasks and processes,while 53%are empowering non-technical employees to meet their own needs.Other strategies include outsourcing IT functions and reskilling existing employees.Empower your organization with an end-to-end automation strategy.Watch now.How organizations are addressing the IT skills gap:Addressing the skills gap with automationINVESTING IN NEW TECHNOLOGIESAUTOMATING TASKS/PROCESSESOUTSOURCING IT FUNCTIONSEMPOWERING NON-TECHNICAL EMPLOYEES TO MEET THEIR OWN IT NEEDSRESKILLING EXISTING EMPLOYEES02 PeoPle:enhancing the emPloyee exPerience14IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEAs more people leave their current roles,organizations struggle to hire people with the right skills.Nearly three quarters(73%)of senior IT leaders agree that acquiring IT talent has never been harder.As a result,the challenge of talent acquisition now influences technology choices for 98%of organizations.and reskilling existing employees.Technology choices driven by talent acquisition challenges73%OF ORGANIZATIONS SAY THAT TALENT ACQUISITION CHALLENGES INFLUENCE TECHNOLOGY INVESTMENT DECISIONS.98%OF SENIOR IT LEADERS AGREE THAT ACQUIRING IT TALENT HAS NEVER BEEN HARDER.02 PeoPle:enhancing the emPloyee exPerience15IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCECompanies are adapting their people strategy to build a workforce that meets the needs of the business today and tomorrow.This means widening their recruitment criteria 80%of senior IT leaders are seeking developer talent from non-traditional backgrounds and focusing on upskilling and reskilling workers.At the same time,86%of senior IT leaders agree that business acumen is an important skill for technical talent.Widening recruitment criteria80 %OF SENIOR IT LEADERS AGREE THAT THEY SEEK DEVELOPER TALENT FROM NON-TRADITIONAL BACKGROUNDS(E.G.,VETERANS,NON-TECHNICAL PEOPLE CHANGING CAREERS,ETC.)OF SENIOR IT LEADERS AGREE THAT BUSINESS ACUMEN IS AN IMPORTANT SKILL FOR TECHNICAL TALENT.02 PeoPle:enhancing the emPloyee exPerience16IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCE17IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEEighty-six percent of senior IT leaders recognize that they must improve their training and development resources to properly upskill and reskill employees in other areas or as an incentive to attract new talent.While people are an important piece of the puzzle,an IT and business strategy also requires efficient processes to be successful.Move to improvedevelopment resources86%OF SENIOR IT LEADERS AGREE THAT THEIR ORGANIZATION NEEDS TO IMPROVE THEIR TRAINING AND SKILL DEVELOPMENT RESOURCES.02 PeoPle:enhancing the emPloyee exPerienceProcess:Bringing IT and business teams together98%2%OF SENIOR IT LEADERS AGREE THAT WORKING PROCESSES BETWEEN IT AND BUSINESS TEAMS COULD BE SIGNIFICANTLY IMPROVED.Improved collaborative processes needed between IT and business teams IT is no longer just a technology enabler;IT now solves business-critical problems,tackles major business objectives,and helps develop competitive advantages with technology.This shift requires IT and business teams to work in closer collaboration for strategic objectives.However,98%of IT leaders said that working processes between IT and business teams could be improved.Learn how IT and business team alignment impacts business outcomes.Download report.03 Process:bringing it and business together19IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEWhere do process improvements fall for IT organizations:Forty-six percent of senior IT leaders say making process improvements is a major priority for their organization over the next 12 months with many reporting that their existing processes are hindering progress.This push to improved processes is particularly prevalent in the communications,media,and technology(CMT)industry(60%),as well as the public sector(52%).Process improvements are a key priority for ITMAJOR PRIORITYMODERATE PRIORITYMINOR PRIORITY46H%6 Process:bringing it and business together20IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEWhere IT processes are hindering the organization:Nine out of ten(91%)senior IT leaders say that existing IT processes are blocking their teams productivity.Process challenges are also hindering innovation,technology adoption,and customer and employee experiences.Existing processes block productivity and transformationCUSTOMER EXPERIENCE32B%EMPLOYEE EXPERIENCE34%TECHNOLOGY ADOPTION349%PRODUCTIVITY31C%INNOVATION368%TO A MAJOR EXTENTTO A MODERATE EXTENTNOT AT ALL03 Process:bringing it and business together21IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEWhere IT processes need to improve to support innovation:Most IT leaders believe processes need to improve across a range of IT functions including data management,integration,and security and governance to support innovation effectively.Innovation hinges on process improvements DATA MANAGEMENT/ANALYTICS6GG%IT INTEGRATION5IF%SOFTWARE DEVELOPMENT7EI%SECURITY&GOVERNANCE6EI%IT SERVICE MANAGEMENT8FF%THEY COULD BE SIGNIFICANTLY IMPROVEDTHEY COULD BE SOMEWHAT IMPROVEDNO ROOM TO BE IMPROVED03 Process:bringing it and business together22IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEHow organizations are using or planning to use fusion teams:To address process challenges,senior IT leaders are looking to create fusion teams.These are multi-disciplinary teams that blend workers with technology,analytics,or domain expertise and who share responsibility for business and technology outcomes.Already 69%of organizations have created or are in the process of rolling out fusion teams,and an additional 22%plan to do so within the next 12 months.Of organizations with fusion teams already in place,63%of IT leaders say these teams have helped the business meet its goals.Fusion teams bridge alignment and drive success37c%OF SENIOR IT LEADERS SAY THAT FUSION TEAMS HAVE HELPED THEIR BUSINESS MEET ITS GOALS.28A%6%3%WE HAVE ALREADY CREATED FUSION TEAMSWE ARE IN THE PROCESS OF CREATING AND ROLLING OUT FUSION TEAMSWE ARE PLANNING TO INTRODUCE FUSION TEAMS IN THE NEXT 12 MONTHSWE ARE PLANNING TO INTRODUCE FUSION TEAMS BEYOND 12 MONTHSWE ARE INTERESTED IN THE CONCEPT BUT HAVE NO PLANS TO INTRODUCE FUSION TEAMS03 Process:bringing it and business together23IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCE24IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEFusion teams also deliver value across the business via improved efficiency,higher employee and customer satisfaction,increased agility,and accelerated innovation.Both people and processes rely heavily on technology to meet business objectives.The next section looks at the ways organizations are using technology to make people and processes more efficient.Fusion teams adding value across the businessExpected outcomes from fusion teams:58WSS%GREATER EFFICIENCY/REDUCED COSTS60%IMPROVED EMPLOYEE SATISFACTIONIMPROVED CUSTOMER SATISFACTIONINCREASED AGILITYACCELERATED INNOVATION03 Process:bringing it and business togetherTechnology:APIs,integration,automation,and low/no-codeALWAYS STANDARD41%MOSTLY STANDARDROUGHLY EVEN SPLIT BETWEEN STANDARD AND CUSTOMIZED26$%MOSTLY CUSTOMIZED9%ALWAYS CUSTOMIZED1%Processes IT organizations use to adopt new software:Implementing new software allows organizations to evaluate their existing processes and standardize them.However,75%of organizations require customized processes when adopting new software to meet their business requirements.This is one of many considerations senior IT leaders face as they look to future-proof their technology stack.New software requires custom implementation processes04 technology:aPis,integration,automation,and loW/no-code tools26IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEOF SENIOR IT LEADERS PREFER TO UPDATE OR UPGRADE EXISTING SOLUTIONS WHEREVER POSSIBLE,RATHER THAN REPLACING THEM.72(%IT is leaning in to existing investments,rather than buying new technologyMost IT leaders(72%)prefer to update or upgrade existing solutions wherever possible,rather than replacing them.With growing economic headwinds,many organizations are looking to extract further value from their existing infrastructure instead of making investments in brand new technology.04 technology:aPis,integration,automation,and loW/no-code tools27IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEBest-of-breed approach can lead to greater IT complexityOrganizations are using best-of-breed technologies to create new customer and employee experiences.While a best-of-breed strategy can increase agility,81%of respondents say that this approach correlates to struggles with IT complexity especially within the healthcare sector(87%).OF IT LEADERS AGREE A BEST-OF-BREED APPROACH MEANS THEIR ORGANIZATION STRUGGLES WITH IT COMPLEXITY.81 technology:aPis,integration,automation,and loW/no-code tools28IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEIntegration projects take too long and cost too muchIn order to create connected experiences,organizations need integration.Two-thirds(66%)of IT leaders believe data or system integration projects take too long and 69%of them say they are too expensive.At the same time,68%of senior IT leaders recognize that a lack of data or system integration creates a disconnected customer experience,which damages customer loyalty and retention.682%OF SENIOR IT LEADERS LEADERS AGREE THAT A LACK OF DATA OR SYSTEM INTEGRATION CREATES A DISCONNECTED CUSTOMER EXPERIENCE WITHIN THEIR ORGANIZATION.664%OF SENIOR IT LEADERS BELIEVE DATA OR SYSTEM INTEGRATION PROJECTS TAKE TOO LONG.691%OF SENIOR IT LEADERS BELIEVE DATA OR SYSTEM INTEGRATION PROJECTS ARE TOO EXPENSIVE.04 technology:aPis,integration,automation,and loW/no-code tools29IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEHow integration concerns influence an organizations decision to build a new application or software in-house:Integration now major factor in purchasing decisionsIntegration struggles are now influencing buying decisions.Most(98%)of senior IT leaders say that new technology investments are influenced by a tools ability to integrate with existing technology.While roughly 90%say that the decision to build or buy is influenced by integration concerns.How integration with existing technology influence IT investment decisions:How integration concerns influence an organizations decision to buy a new application or software:37S%8%1%TO A MAJOR EXTENTTO A MODERATE EXTENTTO A MINOR EXTENTNOT AT ALL599%2%TO A MAJOR EXTENTTO A MODERATE EXTENTTO A MINOR EXTENTNOT AT ALLTO A MAJOR EXTENTTO A MODERATE EXTENTTO A MINOR EXTENTNOT AT ALL41P%8%1 technology:aPis,integration,automation,and loW/no-code tools30IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEHow organizations plan to use low/no-code tools over the next 12 months:Low/no-code tools play a key role in IT strategy Faced with a lack of coding talent,many senior IT leaders are turning to low/no-code tools to enable business users to build and test new experiences.Almost all organizations(96%)currently use low/no-code tools and 36%plan to increase their use over the next 12 months.WE CURRENTLY USE THEM AND PLAN TO INCREASE USEWE CURRENTLY USE THEM AND PLAN TO DECREASE USE36S%WE DONT CURRENTLY USE THEM,BUT PLAN TO7%3%WE CURRENTLY HAVE NO PLANS TO USE THEM2%WE CURRENTLY USE THEM AND PLAN TO MAINTAIN USE04 technology:aPis,integration,automation,and loW/no-code tools31IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCECurrent IT strategies for low/no-code tools:Variety of low/no-code strategies usedIT leaders have various approaches for implementing low/no-code tools.An upfront strategy is used by 32%of organizations in parts of their business,31%have adopted a bottom-up approach driven by developers or systems integrators,while 26%have implemented an upfront strategy across a majority of the business.BOTTOMS-UP APPROACH DRIVEN BY DEVELOPMENT TEAMS/SYSTEM INTEGRATORSUPFRONT STRATEGY THAT IS IMPLEMENTED IN PARTS OF THE BUSINESSUPFRONT STRATEGY THAT IS IMPLEMENTED IN THE MAJORITY OF THE BUSINESSMANDATORY COMPANY-WIDE LOW/NO-CODE STRATEGY FOR ALL PROJECTS312&%7 technology:aPis,integration,automation,and loW/no-code tools32IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEautomation maturity is growing as more organizations turn to automation to enhance customer experiences and product/service quality.two-thirds of organizations(67%)have either automated their it operations and many have introduced automation across other business functions including finance,customer support,marketing,sales,and hr.however,fully automated processes remain quite low with an average of 23%of organizations saying theyve reached this across business functions.Automation is driving process efficiencySee how automation is already revolutionizing the way the world works.Read more.04 technology:aPis,integration,automation,and loW/no-code tools33IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEHow automated are the following business functions:543%77%66%6%4%68%6$5 %5!6$%63!%NO AUTOMATED PROCESSES AT ALLMINORITY OF PROCESSES AUTOMATEDAROUND HALF OF PROCESSES AUTOMATEDMOST PROCESSES AUTOMATEDFULLY AUTOMATED PROCESSESIT OPERATIONSNO AUTOMATED PROCESSES AT ALLMINORITY OF PROCESSES AUTOMATEDAROUND HALF OF PROCESSES AUTOMATEDMOST PROCESSES AUTOMATEDFULLY AUTOMATED PROCESSESCUSTOMER SUPPORTNO AUTOMATED PROCESSES AT ALLMINORITY OF PROCESSES AUTOMATEDAROUND HALF OF PROCESSES AUTOMATEDMOST PROCESSES AUTOMATEDFULLY AUTOMATED PROCESSESMARKETINGNO AUTOMATED PROCESSES AT ALLMINORITY OF PROCESSES AUTOMATEDAROUND HALF OF PROCESSES AUTOMATEDMOST PROCESSES AUTOMATEDFULLY AUTOMATED PROCESSESSALESNO AUTOMATED PROCESSES AT ALLMINORITY OF PROCESSES AUTOMATEDAROUND HALF OF PROCESSES AUTOMATEDMOST PROCESSES AUTOMATEDFULLY AUTOMATED PROCESSESENGINEERINGNO AUTOMATED PROCESSES AT ALLMINORITY OF PROCESSES AUTOMATEDAROUND HALF OF PROCESSES AUTOMATEDMOST PROCESSES AUTOMATEDFULLY AUTOMATED PROCESSESHRNO AUTOMATED PROCESSES AT ALLMINORITY OF PROCESSES AUTOMATEDAROUND HALF OF PROCESSES AUTOMATEDMOST PROCESSES AUTOMATEDFULLY AUTOMATED PROCESSESFINANCENO AUTOMATED PROCESSES AT ALLMINORITY OF PROCESSES AUTOMATEDAROUND HALF OF PROCESSES AUTOMATEDMOST PROCESSES AUTOMATEDFULLY AUTOMATED PROCESSESEMPLOYEE ONBOARDING04 technology:aPis,integration,automation,and loW/no-code tools34IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCETop 5 business priorities over the next 12 months:Information security#1 priority for businessWith new laws and regulations emerging every year requiring businesses to adhere to complex data control requirements,data privacy and protection remain top of mind.Information security is the most significant business priority for organizations in the next 12 months,ahead of digital transformation and cloud strategy.DIGITAL TRANSFORMATIONINFORMATION SECURITY CLOUD STRATEGYDATA STRATEGYCUSTOMER EXPERIENCE280$! technology:aPis,integration,automation,and loW/no-code tools35IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCESecurity degrading customer experienceMore than three-quarters(77%)of senior IT leaders agree that internal security and governance risks are as high as external ones with 83%saying they monitor how employees access and use customer data.However,maintaining high-quality customer experiences while securing data can be a challenge.Sixty-three percent of IT leaders say their security and governance controls degrade the customer experience.637%OF SENIOR IT LEADERS BELIEVE THAT THEIR ORGANIZATIONS SECURITY AND GOVERNANCE CONTROLS DEGRADE CUSTOMER EXPERIENCE.77#%OF SENIOR IT LEADERS AGREE THAT INTERNAL SECURITY AND GOVERNANCE RISKS ARE AS HIGH AS EXTERNAL RISKS.83%OF SENIOR IT LEADERS SAY THAT THEIR ORGANIZATION MONITORS HOW EMPLOYEES ACCESS AND USE CUSTOMER DATA.04 technology:aPis,integration,automation,and loW/no-code tools36IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCE37IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEEighty-three percent of senior IT leaders say public concerns over data privacy have prompted their organization to increase security and governance investments.For 84%,data protection regulations such as GDPR and CPRA have influenced investment decisions.Most organizations are also communicating with customers about their data security strategy with 82%saying they are transparent with customers about how their data is used.Data privacy concerns driving security and governance investments82%OF SENIOR IT LEADERS SAY THEY ARE TRANSPARENT WITH CUSTOMERS ABOUT HOW THEIR DATA IS USED.83%OF SENIOR IT LEADERS SAY PUBLIC CONCERNS OVER DATA PRIVACY HAVE PROMPTED THEIR ORGANIZATION TO INCREASE SECURITY AND GOVERNANCE INVESTMENTS.84%OF SENIOR IT LEADERS AGREE THAT DATA PROTECTION REGULATIONS,SUCH AS GDPR AND CPRA,HAVE PROMPTED THEIR ORGANIZATION TO INCREASE SECURITY AND GOVERNANCE INVESTMENTS.04 technology:aPis,integration,automation,and loW/no-code toolsMeasuring futureIT successOF SENIOR IT LEADERS SAY THEYVE HAD TO DEVELOP NON-IT SKILLS TO BECOME MORE STRATEGIC WITHIN THEIR ORGANIZATION.The role of senior IT leaders has shifted dramatically in recent years more than any other enterprise function.As technology becomes increasingly integral to business success,so has the senior IT leaders influence in shaping organizational strategies in the modern digital era.In a post-pandemic world,senior IT leaders require a skill set that is much broader than technology expertise.The evolving role of senior IT leaders84 measuring Future it success39IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCE53RPHFE%SERVICE AVAILABILITY/SYSTEM PERFORMANCEEMPLOYEE PRODUCTIVITYCOST REDUCTION/OPTIMIZATIONCUSTOMER EXPERIENCEEMPLOYEE EXPERIENCEPROJECT DELIVERYSALESKPIs IT leaders are being measured on:Roughly half of all senior IT leaders are now evaluated on employee productivity(52%),cost reduction and optimization(50%),and customer(48%)and employee experience(46%).This varies between industries;for financial services and insurance,for example,62%of senior IT leaders are measured on employee productivity.IT now measured by productivity,cost reduction,and experiences05 measuring Future it success40IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCE41IT LEADERS PULSE REPORT 2022:FINANCIAL SERVICES AND INSURANCEAs economic conditions remain volatile,senior IT leaders are under pressure to empower their organization to remain agile while focusing on meeting business goals.However,three-quarters(74%)of respondents say project backlogs are preventing them from working on strategic initiatives.To overcome these challenges,organizations need to leverage APIs,automation,and low/no-cost tools to create connected customer and employee experiences.By automating processes where feasible,senior IT leaders can realize value faster,accelerate innovation,and successfully support their organization navigate todays challenging landscape.IT burdened withproject backlogs74&%OF SENIOR IT LEADERS AGREE THAT PROJECT BACKLOGS PREVENT THEM FROM WORKING ON STRATEGIC INITIATIVES.05 measuring Future it success41it leaders Pulse rePort 2022Want to learn more?The state of digital transformation for financial servicesTake a look at the Connectivity Benchmark Report data through a financial services lens to see how digital transformation has become a critical driver of customer engagement and employee productivity.Read the report1I N S I G H T S F R O M T H E 2 0 2 2 C O N N E C T I V I T Y B E N C H M A R K R E P O R TT H R O U G H T H E I N D U S T R Y L E N SThe state of digital transformation for public sector R E P O R T1The state of digital transformation for financial servicesIn collaboration with Deloitte DigitalI N S I G H T S F R O M T H E 2 0 2 2 C O N N E C T I V I T Y B E N C H M A R K R E P O R TT H R O U G H T H E I N D U S T R Y L E N S 62 %$20MR E P O R TDeliver a seamlesscustomer experienceDeliver a seamless customer experienceFind out how MuleSoft Accelerator for Financial Services can help your organization enable real-time relationship management.Watch the webinarModernize legacy banking systemsLearn how banks can use 3 key steps to effectively modernize legacy systems and unlock bigger business results.Get the whitepaperModernizing legacy banking systems:3 key steps to unlock bigger results WHITEPAPERSalesforce,the global CRM leader,empowers companies of every size and industry to digitallytransform and create a 360 view of their customers.For more information about Salesforce(NYSE:CRM),visit .Any unreleased services or features referenced in this or other press releases or public statementsare not currently available and may not be delivered on time or at all.Customers who purchaseSalesforce applications should make their purchase decisions based upon features that arecurrently available.Salesforce has headquarters in San Francisco,with offices in Europe and Asia,and trades on the New York Stock Exchange under the ticker symbol“CRM”.For more information please visit ,or call 1-800-NO-SOFTWARE.MULESOFT IS A REGISTERED TRADEMARK OF MULESOFT,INC.,A SALESFORCE COMPANY.ALL OTHER MARKS ARE THOSE OF RESPECTIVE OWNERS.
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Artificial Intelligence for Drug DiscoveryLandscape OverviewQ1 2022www.deep-pharma.tech2Deep Pharma IntelligenceAI for Drug Discovery Infographic Summary and Mind Maps3Executive Summary17Application of AI for Advanced R&D23Business Activity:Key Trends28Industry developments:Challenges and Forecasts31Business Activity:Overview33Leading Companies by Amount and Stage of Funding3450 Leading Investors in Pharmaceutical AI 40Big Pharmas Focus on AI48AI in Pharma Publicly Traded Companies57Top Publicly Traded Companies Related to AI-Pharma79AI for Advanced R&D:Applications and Use Cases89Top AI Breakthroughs 2018-202190Computational Methods Used by the Most Advanced AI Companies9615 Notable R&D Use Cases of AI Application in Biopharma104Industry Developments 2020-2022142Key Takeaways148Appendix:List of Entities154Overview of Proprietary Analytics by Deep Pharma Intelligence185Disclaimer190This 190-page Artificial Intelligence for Drug Discovery Landscape Overview Q1 2022 report represents the eleventh issue of market analytics focused on the Artificial Intelligence(AI)application in the pharmaceutical research industry.The primary goal of this series of reports is to give a complete picture of the industry environment in terms of AI usage in drug discovery,clinical research,and other elements of pharmaceutical research and development.This overview highlights recent trends and insights in the form of helpful mind maps and infographics and gauges the performance of prominent players who shape the industrys space and relationships.It can help the reader comprehend what is going on in the sector and potentially predict what will happen next.Since the last edition,data has been significantly updated to reflect the fast-paced market dynamics and an overall increase in pharmaceutical AI investment and business development activities.The listings of AI-biotech businesses,biotech investors,and pharmaceutical organizations have been expanded to reflect the pharmaceutical industrys rising interest in sophisticated data analytics technology.Alongside investment and business trends,the report also provides technical insights into some of the latest AI applications and research achievements.2Deep Pharma IntelligenceTable of ContentsIntroductionClinical DevelopmentData ProcessingEarly Drug DevelopmentEnd-to-end Drug DevelopmentInvestorsAI CompaniesCorporationsDEEP PHARMA INTELLIGENCEPharmaTechCROPreclinical DevelopmentDrug RepurposingArtificial Intelligence for Drug DiscoveryLandscape Overview Q1 2022AI Companies-495Investors-1120Corporations-100Selected Pharma AI Deals AI Companies Pharma CorporationsAI Companies 44Deep Pharma IntelligencePharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.Selected Pharma AI Deals 5Deep Pharma IntelligenceAI Companies Pharma CorporationsAI Companies Pharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.Comparison of Top-40 Leading AI for Drug Discovery Companies Expertise in Drug Discovery R&DAdvanced AI tools for specific Use CasesAdvanced AI systems with multiple models End-to-end AIExpertise in Drug DiscoveryExpertise in AI6Deep Pharma IntelligenceClinical pipeline(phase 1-2)Validated R&D Use casesand preclinical pipeline40 Leading Companies in AI for Drug Discovery SectorDeep Pharma IntelligenceAtomwiseAbCelleraAI TherapeuticsBeacon Biosignals3BIGS1A2APharma 234AnimaBiotech5Ardigen6AriaPharmaceuticals7Auransa8910Benevolent AlBergBioage Labs1112Berkeley Lights1314 Biovista15 Black Diamond Therapeutics16 ConcertAl17 Cyclica18 CytoReason19 Deargen20EnvisagenicsExscientiaNuritasDeepGenomics21DeepMindHealth222324Healx25Insillico Medicine26Insitro27Lantern Pharma28Neumora2930PharnextRecursionSchrodinger 3132ReviveMed3334 SensyneHealth35 Silicon36 Standigm37 Turbine38 Valo39 XtalPi40Deep Pharma Intelligence750 Leading Investors in AI for Drug Discovery Sector8Deep Pharma IntelligenceKhosla VenturesY CombinatorPerceptive AdvisorsEU EASMECasdin Capital1GV234Alexandria Venture Investments5DCVC6SoftBank Vision Fund 7Andreessen Horowitz89108VCARCH Venture PartnersForesite Capital1112Bill&Melinda Gates Foundation1314SOSV15T.Rowe Price16ZhenFund17AME Cloud Ventures18F-Prime Capital19Felicis Ventures20Obvious VenturesOrbiMedNew Enterprise AssociatesFounders Fund21General Catalyst222324B Capital Group25DCVC Bio26Lifeforce Capital27Lilly Asia Ventures28Lux Capital2930TencentWuxi App TechEDBI3132Baidu Ventures3334Novo Holdings35Tried Rock Ventures36Amadeus Capital Partners37Amgen Ventures386 Dimensions Capital39Baillie Gifford 40OS FundRevolutionOctopus Ventures GT Healthcare Capital41Inovia Capital4344Two Sigma Ventures45Sequoia Capital Channel46Bristol-Myers Squibb47EPIC Ventures48Celgene495042Sources:Investment Digest AI in Pharma495 AI Companies:Regional ProportionThe US is still firmly in the lead regarding its proportion of AI for Drug Discovery companies.Interestingly,Asia and the Middle East continue to expand usage of AI technologies in the Pharmaceutical Industry.The ratio of companies that use AI for Drug Development in the UK and European countries is decreasing compared to the Asian market.The Asia-Pacific region continues to aggressively increase the number of AI for Drug Discovery Companies,particularly in China,and this tendency will probably maintain.9Deep Pharma IntelligenceUS55.15nada4.64%UK8.89%China3.83%Asia&Middle East9.89%Australia0.40%EU16.7620 Investors:Regional ProportionThe United States continues to lead the rest of the world in terms of artificial intelligence for companies and funds that invest in Drug Discovery.This is reasonable,given that more than a half of the worlds AI for Drug Discovery companies have their headquarters in USA.Comparing with previous periods of 2021,we can observe significant growth of the number of investors in China,as well as in US as Europe.Thus,together with UK these regions are leaders by the number of investors in AI in Drug Discovery companies.10Deep Pharma IntelligenceCanada4.19%US56.19%UK6.77%EU13.45%China8.19%Asia&Middle East3.03%South Korea1.96%Singapore2.23%Japan1.6%Australia1.16P Leading Investors:Regional ProportionThe United States continues to lead the rest of the world in terms of artificial intelligence for companies and funds that invest in Drug Discovery.This is reasonable,given that more than a half of the worlds AI for Drug Discovery companies have their headquarters in USA.During 2021 we can observe significant growth of the number of investors in Asia,mainly in China,Hong Kong and Singapore.The USA,the UK,Canada and EU remain to be leaders by the number of investors in AI in Pharma companies.11Deep Pharma IntelligenceCanada4.0%UK8.0%EU4.0%US68.0%China14.0%Singapore2.0%Sources:Investment Digest AI in Pharma36 Pharma Corporations Applying Advanced AI in Healthcare and Drug DiscoveryThe industry is seeing an increasing level of regional diversification.Whereas historically the US has dominated the AI for Drug Discovery race in terms of the number of AI companies,the volume of investments and number of industry specialized conferences,from 2019 we are seeing an increased level of activity from the UK,Switzerland and China.12Deep Pharma IntelligenceUK2.7%EU19.4%US41.6%Asia&Middle East36.10 Leading Tech Corporations:Regional ProportionThe US is the leader according to the number of tech corporations applying advanced AI in healthcare and drug discovery.EU leads the world in terms of the number of Chemical Corporations.The second biggest figure can be observed in Asia while the EU is in the third place.This is sensible within the context of the recent increase in the chemical industry in EU that overweight the US and Asian markets of chemical substances and related products.A lot of these chemical corporations are participating in cooperation and partnerships that are aimed at drug discovery and are related to pharmaceutical issues.13Deep Pharma IntelligenceUK3.3%EU13.3%US50.0%Asia&Middle East30.0nada3.3%Pharmas“AlphaGo Moment”14Deep Pharma Intelligence1990201220152018-192020-21Practical ValueFundamental breakthroughs in AI theory (DL,NLP,etc)ImageNet and the rise of practical DLGANs and other advanced NN structures“AlphaGo Moment”in pharma:practical validation in drug design,biotech R&DMature technology.Strategic competition for AI startups,a rising wave of investments/M&A deals2022-23Widespread adoption of AI in pharma (VR,digital twin,automated data analysis etc.),prioritisation of AI in R&D Experimental ResultComputational PredictionNotable Breakthroughs in AI for Pharma15Deep Pharma IntelligenceDeep Genomics AI-driven platform predicted novel target and oligonucleotide candidate for Wilson disease in under 18 months.Insilico Medicine applied generative adversarial network-based system GENTRL for rapid identification of potent DDR1 Kinase inhibitors within 21 days.DeepMinds AlfaFold learns to predict proteins 3D shape from its amino-acid sequence,a 50 year-old grand challenge in biology.The University of Washington has developed a deep learning model,“RoseTTAFold”,that calculates protein structure on a single gaming computer within 10 minutes.Model201920202021ExperimentTechnological Advancements Defining the Market16Deep Pharma IntelligenceInsilico Medicine achieved industry-first fully AI-based Preclinical Candidate.Initial hypothesis was build via DNN analysis of omics and clinical datasets of patients.After that company used its AI PandaOmics engine for target discovery,analyzing all relevant data,including patents and research publications with NLP algorithms.In the next step Insilico has applied its generative chemistry module(Chemistry42)in order to design a library of small molecules that bind to the novel intracellular target revealed by PandaOmics.The series of novel small molecules generated by Chemistry42 showed promising on target inhibition.One particular hit ISM001 demonstrated activity with nanomolar(nM)IC50 values.When optimizing ISM001,Insilico managed to achieve increased solubility,good ADME properties,and no sign of CYP inhibition with retained nanomolar potency.Interestingly,the optimized compounds also showed nanomolar potency against nine other targets related to fibrosis.The efficacy and a good safety of the molecule led to its nomination as a pre-clinical drug candidate in December 2020 for IND-enabling studies.The phase I clinical trial for the novel drug candidate is planned for December 2021.1 week2 months4 months11 monthsPhase 1Phase 2Phase 3Submission to launchDisease Hypothesis&Target IdentificationTarget-to-hitHit-to-leadLead optimization and Candidate ValidationPreclinical candidate Selected(PCC)Up to Decades1 year1.5 years2 yearsPhase 1Phase 2Phase 3Submission to launchInsilico$50kN/A$200k$94M$400k$166M$200k$414MTraditional ApproachSource:Insilico Medicine 2021Executive SummaryDEEP PHARMA INTELLIGENCEThis 190-page“Artificial Intelligence for Drug Discovery Landscape Overview,Q1 2022”report marks the installment in a series of reports on the topic of the Artificial Intelligence(AI)application in pharmaceutical research industry that DPI have been producing since 2017.The main aim of this series of reports is to provide a comprehensive overview of the industry landscape in what pertains adoption of AI in drug discovery,clinical research and other aspects of pharmaceutical R&D.This overview highlights trends and insights in a form of informative mind maps and infographics as well as benchmarks the performance of key players that form the space and relations within the industry.This is an overview analysis to help the reader understand what is happening in the industry nowadays and possibly give an idea of what is coming next.Alongside investment and business trends,the report also provides technical insights into some of the latest achievements in the AI application and research.Report at a Glance18Deep Pharma IntelligenceLatest AchievementsNotable Case StudiesAI-Pharma CollaborationsBusiness TrendsTechnical InsightsCROsTech Companies495 AI BioTech Companies1120 InvestorsPharma CompaniesPharma Efficiency:Challenges19Deep Pharma Intelligence10 years $2.6 bln=1 new drugIt takes on average over 10 years to bring a new drug to market.As of 2014,according to Tufts Center for the Study of Drug Development(CSDD),the cost of developing a new prescription drug that gains market approval is approximately$2.6 billion.This is 145%increase,correcting for inflation,comparing to the same report made in 2003.The pharmaceutical industry is in a terminal decline,and the returns on new drugs that do get to market do not justify the massive investments that Pharma currently puts into R&D anymore.The solution to this problem comes from three key strategies:evolution of business models towards more collaboration and pipeline diversification early implementation of AI as a universal shift towards data-centric drug discovery discovery of new therapeutic modalities(biologics,therapies etc.)0-Effect on bodyI-Safety in humansII-Effectiveness at treating diseasesIII-Larger scale safety and effectivenessIV-Long term safety1 approved drug5,000,000 compounds500 compounds5FDAPre-clinical developmentClinical developmentRegulatory approvalResultDrug discoverySource:Conflict of Interest in Medical Research,Education,and Practice,Computer-aided Drug Design20Deep Pharma IntelligenceScreeningModellingDe novo designTodays task for the pharma industry is to create a cheap and effective solution for drug development,companies apply various computational methods to reach that goal.Computer-aided drug design(CADD)is a modern computational technique used in the drug discovery process to identify and develop a potential lead.CADD includes computational chemistry,molecular modeling,molecular design and rational drug design.Sources:Advantages of Structure-Based Drug Design Approaches in Neurological Disorders.CADDDiscoveryMolecule SelectionOptimizationDatabasesValidationTarget Identification Homology Modelling Molecular Modelling Structure based Shape based Pharmacophore based Druggability pocket Compound identification QSAR Lead Optimization Docking and scoring Library design Affinity evaluation ADME estimation Small molecule databases 3D structure database Molecular fragment databaseComputer-aided Drug Design21Deep Pharma IntelligenceDatabasesSmall molecule databases3D structure databasesMolecular fragment databasesStructure-based virtual screeningBinding energy analysisScoringDockingChemical intuitionMolecular dynamic simulationAnalyze the interaction of target structure and lead candidateModellingHomology ModellingMolecular ModellingFunctional GenomicsTarget protein identificationBinding site predictionSources:Advantages of Structure-Based Drug Design Approaches in Neurological Disorders.Modern computational structure-based drug design has established novel platforms that mostly have a similar structure for testing drug candidates.The usage of AI can simplify and facilitate the drug design from filtering datasets for appropriate compounds to advanced lead modification and in silico testings.Big Pharmas AI-focused partnerships till Q4 2021In this report we have profiled 470 actively developing AI-driven biotech companies.A steady growth in the AI for Drug Discovery sector can be observed in terms of substantially increased amount of investment capital pouring into the AI-driven biotech companies($2.28B in HY 2020 against$2.93B in HY 2021),the increasing number of research partnerships between leading pharma organizations and AI-biotechs,and AI-technology vendors,a continuing pipeline of industry developments,research breakthroughs,and proof of concept studies,as well as exploding attention of leading media and consulting companies to the topic of AI in Pharma and healthcare.Some of the leading pharma executives increasingly see AI as not only a tool for lead identification,but also a more general tool to boost biology research and identify new biological targets and develop novel disease models.The main focus of AI research for today is still on small molecules as a therapeutic modality.22Deep Pharma Intelligence25 Deals6-10 Deals11-15 Deals16-20 DealsApplication of AI for Advanced R&D to Address Pharma Efficiency Challenges23Deep Pharma IntelligenceClinical TrialsAI for Advanced R&DTarget Discovery and Early Drug Discovery Aggregation and Synthesis of InformationRepurposing of Existing DrugsDesign and Processing of Preclinical ExperimentsAccelerated development of new drugs and targets identification Identify novel drug candidates Analyze data from patient samples Predict pharmacological properties Simplify protein designTime-and resources-efficient information management Generate insights from thousands of unrelated data sources Improve decision-making Eliminate blind spots in researchSearching for new applications of existing drugs at a high scale Rapidly identify new indications Match existing drugs with rare diseases Testing 1000 of compounds in 100 of cellular disease models in parallelOptimization of experiments and data processing Reduce time and cost of planning Decode open-and closed-access data Automate selection,manipulation,and analysis of cells Automate sample analysis with a robotic cloud laboratoryTargeted towards personalized approach and optimal data handling Optimize clinical trial study design Patient-representative computer models Define best personalized treatment Analyze medical records Improve pathology analysisBusiness ActivityInsitro has raised$400M for machine learning-powered drug discovery efforts.The financing was led by the Canada Pension Plan Investment Board with additional backing from Andreessen Horowitz,Casdin CapitalValo Health announced the final closing of its Series B at$300M,including a$110 million investment from Koch Disruptive Technologies(KDT).This brings the overall funding of Valo to over$450M to accelerate the creation of life-changing drugsAmgen Mila partnership that permits Amgen to expand its knowledge of AI and deep learning by interacting and engaging with experts in Milas unique ecosystemIktos announced the application of its AI technology for de novo design for selected Pfizer small-molecule discovery programsZebiAI Therapeutics Relay Therapeutics:Relay bought ZebiAI for$85M upfront and a further$185M in potential milestone paymentsRoivant Silicon Therapeutics:Roivant bought Silicon for$450M along with its physics-based platform for in silico small-molecule drug design.This platform will be integrated with Roivants machine learning developments24Deep Pharma IntelligenceThe business activity has been increasing in the pharmaceutical AI space over Q1 2021-Q1 2022,judging by an increased number of transactions and partnership announcements in this period.The most significant deals and collaborations in include:Partnerships like these provide a huge effect on Pharma industry and are needed in case if a company intends to become a leader in the ongoing competition.Nvidia and Schrdinger have partnered to increase the speed and accuracy of Schrdingers molecule prediction software BenevolentAI and AstraZeneca have extended their partnership to achieve collaboration milestone with novel AI-generated chronic kidney disease targetLantern Pharma and Actuate Therapeutics have entered into collaboration to generate novel intellectual property that will be jointly owned by the companiesAstraZeneca,Merck,Pfizer and Teva formed AION Labs,the innovative lab that will create and adopt AI technology to transform the process of drug discoveryExscientia has signed a research collaboration with Sanofi and received an investment of$100M to develop potential drug candidates for cancer and immune-mediated diseasesValo Health started partnership with Charles River Laboratories to accelerate preclinical drug discovery using Valos small molecule DD platformGenerate Biomedicines,an AI driven drug development company specializing on protein and peptides therapeutics raised$370M on series B funding round,after which entered the collaboration with AmgenBusiness Activity25Deep Pharma IntelligenceDynamics of Investments in AI in PharmaThere has been a substantial increase in the amount of capital invested in AI-driven pharma companies since 2014.During the last seven years,the annual amount of investments in 452 companies has increased by almost 16 times(to$12.73B in total as of December 2021).In 2020,the flow of investments increased by 40%compared to the previous year.The estimated amount of investments in the AI in Pharma sub-sector of the Longevity industry is about to triple this year compared to 2020 which identifies strong investors(foremost VCs)interest in this field regardless of risks.26InvestTech Advanced SolutionsDeep Pharma Intelligence20142015201620172018201920202021Top 10 AI in Pharma Companies by Total Investments in 202127InvestTech Advanced SolutionsDeep Pharma IntelligenceThe chart shows the top 10 AI-driven drug discovery companies sorted by the total funding raised by the end of 2021.Roivant Sciences,a leader in artificial intelligence and precision medicine,is now at the top of the list.Having completed the business combination with Montes Archimedes Acquisition Corp,Roivant Sciences has the total funding raised to$2.09B.Zai lab,chinese provider of transformative medicines could finance$0.8B in capital market.Tempus,Insitro and ThoughtSpot are new companies due to late-stage mega-rounds during the 2021.Major Observations for Q1 2021-Q1 2022:Key Business TakeawaysThe segment of pharmaceutical AI continues consolidation with the increasing number of later stage mega-rounds,including XtalPi and Insitro(both$400M),Generate Biomedicines($370M),Exscientia and Insilico Medicine(both$255M),and Arbor Biotechnologies($215M).The AI start-up pack is clear leaders with significant resources,financial leverage,technical edge,and laggards with fewer finances,technology,and scientific assets.Notably,the BioTech business adopts a new robust trend of taking firms public through SPACs(SPACs).Recently,Roivant Sciences,an AI-driven firm,exited through SPAC.Roivants consolidated cash position will be about$2.5B on September 30,2021.28Deep Pharma IntelligenceThe pharmaceutical AI business is“heating up”,becoming a profitable area for expert biotech investors as well as investor groups looking to diversify their portfolios with high-risk/high-reward firms.The total amount invested in AI in Pharma in 2021 has quadrupled from$4,7M to$12,73M.A growing number of proof-of-concept breakthroughs confirm that AI technology has matured enough to provide tangible value to pharma and contract research organizations(CROs).Due to quickly growing proof of AI tech feasibility and innovation potential,big pharma and contract research organizations are actively competing for AI collaborations.Valo Health started partnership with Charles River Laboratories to accelerate preclinical drug discovery using Valos small molecule Drug Discovery platform.Exscientia has signed a research collaboration with Sanofi and received an investment of$100M to develop potential drug candidates for cancer and immune-mediated diseases.Major Observations for Q1 2021-Q1 2022:Key Business TakeawaysThe global COVID-19 pandemic prolongs the rise of the overall biotech and drug discovery sectors.During 2021 we have observed over 100 medium and large funding rounds for biotech and drug design companies,especially those focused on antiviral therapies and vaccines.29Deep Pharma IntelligenceIn 2021,10 companies that use AI for DD reached IPO status.New York-based Roivant Science closed its IPO in October and raised$611M.Exscientia,a pharmatech company that uses an end-to-end AI platform to design and discover new drugs launched IPO the same day as Roivant Science and raised$350M.The vast majority of companies started gaining IPO status after 2018,marked by a growth of 136.0%during the last four years and we expect this trend growth to continue.When some of the companies complete IPOs in the nearest future,it will attract a significant number of non-biotech investors to enter the Life Sciences sector.The prospects of this trend are already vivid:big tech companies enter partnerships with both innovative start-ups and pharma companies to consolidate resources,mainly in personalized medicine,cell and gene therapy,and molecule prediction software.Some of these companies even open subsidiaries harvesting AI in Drug Design(like Isomorphic Labs from Google).The growing industry traction,reflected in the increasing number of R&D partnerships between big pharma and CROs with AI startups,is a sign that the market is maturing for rapid increase in M&A activity in the nearest future.Despite the crisis,AI-in-Pharma publicly traded companies present YTD growth with reaching$110B of cumulative capitalization as of December 30,2021.Key Technology Takeaways1.AI is regarded by some top executives at big pharma(GSK and others)as a tool to uncover not only new molecules,but also new targets.Ability of deep neural networks to build ontologies from multimodal data(e.g.“omics”data)is believed to be among the most disruptive areas for AI in drug discovery,alongside with data mining from unstructured data,like text(using natural language processing,NLP).2.There is a considerable trend for“AI democratization”where various machine learning/deep learning technologies become available in pre-trained,pre-configured“of-the-shelf”formats,or in relatively ready-to-use formats via cloud-based models,frameworks,and drag-and-drop AI-pipeline building platforms(for example,KNIME).This is among key factors in the acceleration of AI adoption by the pharmaceutical organizations where a non-AI experts can potentially use fairly advanced data analytics tools for their research.3.Proof-of-concept projects keep yielding successful results in research studies,and in the commercial partnerships alike.For example,companies like Recursion Pharmaceuticals,Insilico Medicine,Deep Genomics,and Exscientia achieved important research milestones using their AI-based drug design platforms.30Deep Pharma IntelligenceAI democra-tizationsAI platforms yield successful resultsAI on different steps in DDAi is used not only for drug design,but also target identification.Many AI-designed drugs showed successful results in research studies and even clinical trials.Ready-to-use AI platforms for DD became available and can be used by non-AI experts.AI in Biotech ChallengesLack of Quality DataObstacles That Still Remain31Deep Pharma Intelligence1.Global shortage of AI talent continues to be a serious challenge for the biopharma industry,repeating the trend from our previous reports.While big pharmaceutical companies invest substantial capital in recruitment of AI specialists,still the majority of them are acquired by large tech corporations(Google,Amazon,Alibaba,Tencent,Baidu etc.)However,a growing wave of specialized university programs and courses,geared towards data science and AI application,is projected to address this issue to certain extent in the coming years.2.Lack of available quality data is still a challenge for the unleashing full potential of deep learning technologies.Numerous variations of deep learning(DL)are believed to be the most lucrative area of AI for applications such as drug discovery and clinical research.The key challenge is that DL algorithms are“data-greedy”,while big data in biotech is not always well-versed for modeling,or is inaccessible due to privacy reasons.3.Ethical,legal,and regulatory issues for AI adoption in the pharmaceutical sciences.This set of challenges is related to the previous point,but also includes other questions AI explainability,patentability of AI-generated results,non-optimal regulations in various countries,slowing down the progress and adoption of AI technologies in general,and in the pharmaceutical industry in particular.Lack of Specialists Ethical,Legal and Regulatory IssuesAI in the Global Context32Deep Pharma IntelligenceUS is a main player in AI industry In the beginning of AI implementation,US was a pioneer and then the main player with the greatest number of companies using AI to force R&D,research centres and institutes,and investments.China engages in extensive investment activityIn particular,it has promised to invest$5B in AI.Tianjin,one of the biggest municipalities,is going to invest$16B in its local AI industry,and the Beijing authorities will build$2.12B AI development project.China plans to become the world AI leader by 2030According to the AI Strategic Plan released in July 2017.The analysis of the the Asia-Pacific region has shown that the main forcers of AI implementation include Saama Technologies,Inc.,a leading clinical data analytics company.Europe has traditionally been a strong breeding ground for biopharma activity The UK and EU activity in the pharmaceutical AI race is mainly boosted by Novartis.UK-based BenevolentAI and AstraZeneca collaborate with novel AI-generated chronic kidney disease target.DEEP PHARMA INTELLIGENCEBusiness Activity:OverviewLeading Companies by Amount and Stage of Funding Round A Round B Round C IPO34Deep Pharma IntelligenceApollo Hospitals EnterpriseSchrodinger$463M$4.9MFunding amount prior to the last dealFunding amount by the last dealApollo Hospitals EnterpriseBiofourmis$44M$100MApollo Hospitals EnterpriseRelay Therapeutics$120M$400MBerkeley Lights$178M$95MApollo Hospitals EnterpriseBenevolentAI$202M$90MApollo Hospitals EnterpriseExscientia$375M$100MInsitro$343M$400MXtalPi$400MAtomwise$174.3M$110MXtalPi$386MValo Health$300M$2.3MLeading Companies by Amount and Stage of Funding Round A Round B Round C IPO35Deep Pharma Intelligence$300MApollo Hospitals EnterpriseInsilico Medicine$50M$255M$110MiCarbonX$155M$45MApollo Hospitals EnterpriseNimbus Therapeutics$197M$105M$20MCellarity$50M$123M$90MPathAI$90M$165MIndegene$200MStandigm$61.2MPatSnap$51.6MAetion$93.6MBIOAGE LABS$123MFunding amount prior to the last dealFunding amount by the last deal$10MLeading Companies by Amount and Stage of Funding Round A Round B Round C IPO36Deep Pharma Intelligence$80MStoneWise$100MStrateos$56.1MGENFIT$83MSangamo Therapeutics$8MApollo Hospitals Enterprise$0M$13MRecursion Pharmaceuticals$239M$225MDNAnexus$173M$100Mnference$119M$7MTurbine AI$88MNeuron23$34MStoneWiseStrateosGENFITSangamo Therapeutics$10M$45.7M$11M$85.2MDatavant$40M$40.5MFunding amount prior to the last dealFunding amount by the last deal$61MLeading Companies by Amount and Stage of Funding Round A Round B Round C IPO37Deep Pharma IntelligenceApollo Hospitals EnterpriseRoivant Sciences$1 900M$200MApollo Hospitals EnterpriseTempus$850M$250MApollo Hospitals EnterpriseHuman Longevity$300M$30MSynergy Pharmaceuticals$107M$300MApollo Hospitals EnterpriseGritstone Oncology$341M$55MApollo Hospitals EnterpriseFlatiron Health$313M$11.9MErasca$264M$36MSOPHiA GENETICS$140M$110MITeos Therapeutics$125M$125MBiodesix$30M$209.7MFunding amount prior to the last dealFunding amount by the last dealLeading Companies by Amount and Stage of Funding Round A Round B Round C IPO38Deep Pharma IntelligenceIDEAYA Biosciences$140M$86MApollo Hospitals EnterpriseNeon Therapeutics$125M$36MNeumora Therapeutics$500M$62M$76MApollo Hospitals EnterpriseMedable$203M$304MFoundation Medicine$40M$56MOwkinAlector$62M$133MAi Therapeutics$40M$58MArrakis Therapeutics$38M$75MFunding amount prior to the last dealFunding amount by the last dealProscia$11M$23MOwkin$74.1M$180MDeep Pharma IntelligenceAI for Drug Discovery Market Timeline39 The first scalable AI approaches for Drug Discovery developed and several industry players with forward-thinking executives started launching pilot collaborations and making small investments.However,only few market players believed in the technology.The First AI ApproachesCriticism Market Cap GrowthTransition from Quantity to QualityIntensive Competition Platform-based Drug Design Because AI is still a young approach within the life sciences,many pilot projects failed,creating a lot of criticism towards using deep learning for Drug Discovery and Advanced R&D.Since then,the race to acquire the best AI startups began.Testing of the technology began.The capitalization of the industry was continuously growing.Many bets of early investors appeared to be justified.Over the next several years,we expect to see VC firms and subsidiary funds focused solely on the AI for Drug Discovery subsector and invest in a maximally-diverse number of AI for Drug Discovery companies.Transitioning from the quantity of AI-related collaborations,investments,and M&As to qualitative gains the first practical validations of previously conducted research might be appearing during this year.Competition for the most successful pharma AI companies will increase drastically.Pretty much all big players in the pharma industry are concerned with AI adoption,and tech has become a strategic priority,among other things.Cutting-edge advancements in AI technology with human-like environment simulations in drug discovery.Leading pharmaceutical industry players will be moving towards platform-based drug design.The emergence of comprehensive R&D and business infrastructure enables end-to-end AI-driven drug development.201820192020-20212013-20152016-20172022-202350 Leading Investors in Pharmaceutical AIDEEP PHARMA INTELLIGENCEDKA41Deep Pharma IntelligenceKhosla VenturesY CombinatorPerceptive AdvisorsEU EASMECasdin Capital1GV234Alexandria Venture Investments5DCVC6SoftBank Vision Fund 7Andreessen Horowitz89108VCARCH Venture PartnersForesite Capital1112Bill&Melinda Gates Foundation1314SOSV15T.Rowe Price16ZhenFund17AME Cloud Ventures18F-Prime Capital19Felicis Ventures20Obvious VenturesOrbiMedNew Enterprise AssociatesFounders Fund21General Catalyst222324B Capital Group25DCVC Bio26Lifeforce Capital27Lilly Asia Ventures28Lux Capital2930TencentWuxi App TechEDBI3132Baidu Ventures3334Novo Holdings35Tried Rock Ventures36Amadeus Capital Partners37Amgen Ventures386 Dimensions Capital39Baillie Gifford 40OS FundRevolutionOctopus Ventures GT Healthcare Capital41Inovia Capital4344Two Sigma Ventures45Sequoia Capital Channel46Bristol-Myers Squibb47EPIC Ventures48Celgene495042Sources:Investment Digest AI in Pharma50 Leading Investors in AI for Drug Discovery SectorTop-50 AI in Pharma Investors42Deep Pharma IntelligenceInovia CapitalMontral,Quebec,CanadaObvious VenturesSan Francisco,California,USLifeforce CapitalSan Francisco,California,USSan FranciscoAlexandria VentureSan Francisco,California,USForesite CapitalSan Francisco,California,USFounders FundSan Francisco,California,US8VCSan Francisco,California,USDCVC BioSan Francisco,California,USDCVCSan Francisco,California,USNew YorkCasdin CapitalNew York,New York,USTwo Sigma VenturesNew York,New York,USLux CapitalNew York,New York,USPerceptive AdvisorsNew York,New York,USBristol-Myers SquibbNew York,New York,USOrbiMedNew York,New York,USMountain ViewY CombinatorMountain View,California,USGVMountain View,California,USPalo AltoAME CLoud VenturesPalo Alto,California,USLili VenturesIndianapolis,Indiana,USSOSVPrinceton,New Jersey,USBill&Melinda Gates FoundationSeattle,Washington,UST.Rowe PriceBaltimore,Maryland,USCelgeneSummit,New Jersey,USRevolutionWashington,District of Columbia,USEPIC VenturesSalt Lake City,Utah,USOther StatesManhattan BeachB Capital GroupManhattan Beach,California,USMenlo ParkNew Enterprise AssociatesMenlo Park,California,USAndreessen HorowitzMenlo Park,California,USFelicis VenturesMenlo Park,California,USKhosla VenturesMenlo Park,California,USMassachusettsThird Rock VenturesBoston,Massachusetts,USSR OneCambridge,Massachusetts,USGeneral CatalystCambridge,Massachusetts,USF-Prime CapitalCambridge,Massachusetts,USAlexandria Venture InvestmentsPasadena,California,USIllinoisOS FundPark Ridge,Illinois,USARCH Venture PartnersChicago,Illinois,USEASME Brussels,Brussels Hoofdstedelijk Gewest,BelgiumEDBISingapore,Central RegionNovo HoldingsHellerup,Hovedstaden,DenmarkAmadeus Capital PartnersLondon,England,The UKSoftBank Vision FundLondon,England,The UKBaillie GiffordEdinburgh,Edinburgh,The UKOctopus VenturesLondon,England,The UKBeijingZhenFundBeijing,ChinaBaidu VenturesBeijing,ChinaSequoia Capital ChinaBeijing,ChinaShanghaiWuXi AppTecShanghai,ChinaLilly Asia VenturesShanghai,China6 Dimensions CapitalShanghai,ChinaGT Healthcare Capital PartnersCentral,Hong Kong Island,Hong KongSources:Investment Digest AI in PharmaTop-50 Investors in AI CompaniesINVESTORSINVESTMENTS OVERALLAI FOR DRUG DISCOVERY COMPANIESINVESTED INCasdin Capital2020Absci,Alector,Arzeda,Beacon Biosignals,Celsius Therapeutics,Clover Therapeutics,Exscientia,Gritstone Oncology,Fabric Genomics,Flatiron Health,Foundation Medicine,Lunit,Insitro,Paige,Recursion Pharmaceuticals,Relay Therapeutics,Sema4,ShouTi,SomaLogic,Treeline BiosciencesGV3016Alector,Arrakis Therapeutics,Celsius Therapeutics,DNAnexus,Gritstone Oncology,IDEAYA Biosciences,Insitro,Flatiron Health,Foundation Medicine,Owkin,Relay Therapeutics,Schrdinger,Strateos,Treeline Biosciences,Ultromics,ZappRxY Combinator2415Arpeggio Bio,Athelas,Atomwise,CloudMedx,Coral Genomics,HistoWiz,iLabService,Menten AI,Notable Labs,Ochre Bio,PostEra,Reverie Labs,Segmed,Stratos,Verge GenomicsPerceptive Advisors1313Absci,Alector,Black Diamond Therapeutics,Champions Oncology,DNAnexus,Icosavax,IDEAYA Biosciences,Neuron23,Saama,Sema4,Soma Logic,Relay TherapeuticsAlexandria Venture Investments1212 Arrakis Therapeutics,Celsius Therapeutics,Deep Genomics,GNS Healthcare,Gritstone Oncology,IDEAYA Biosciences,Immunai,Insitro,Fountain Therapeutics,LEXEO Therapeutics,Neuromora Therapeutics,Veralox Therapeutics1710 AbCellera Biologics,Asimov,Atomwise,Auransa,Empirico,Frontier Medicines,Strateos,Unlearn.AI,Frontier Medicines,X-37SoftBank Vision Fund1110Biofourmis,Datavant,Deep Genomics,Exscientia,Insitro,PatSnap,Relay Therapeutics,Roivant Sciences,XtalPiKhosla Ventures139Arpeggio Bio,Atomwise,BIOAGE LABS,Fountain Therapeutics,Deep Genomics,Menten AI,Ochre Bio,Scipher Medicine,ThoughtSpotAndreessen Horowitz138Aria Pharmaceuticals,Asimow,BigHat Biosciences,BIOAGE LABS,Erasca,Flatiron HealthGenesis Therapeutics,InsitroEU Executive Agency for SMEs108Acellera,CellPly,Cytox,Genialis,Genome Biologics,Iris.ai,Optellum,Quibim43Deep Pharma IntelligenceDCVCTop-50 Investors in AI Companies44Deep Pharma IntelligenceINVESTORSINVESTMENTS OVERALLAI FOR DRUG DISCOVERY COMPANIES INVESTED IN8VC107 BigHat Biosciences,Coral Genomics,Immunai,Model Medicine,Notable,ProteinQure,Unlearn.AI ARCH Venture Partners157Arbor Biotechnologies,Generate Biomedicines,Glympse Bio,Erasca,Hangzhou Just Biotherapeutics(Just China),Insitro,Treeline Biosciences Bill&Melinda Gates Foundation137Atomwise,Evotec,Exscientia,Foundation Medicine,Novartis,Schrdinger,Takeda Foresite Capital77Aetion,Alector,DNAnexus,Generate Biomedicines,Insitro,Relay Therapeutics,Wave Life Sciences SOSV247 A2A Pharmaceuticals,Gatehouse Bio,Guided Clarity,Mendel.ai,Stelvio Therapeutics,Strados,Synthace T.Rowe Price87 Arbor Biotechnologies,Generate Biomedicines,Genesis Therapeutics,Insitro,Sema4,SomaLogic,Tempus ZhenFund97AccutarBio,Deep Intelligent Pharma,HistoWiz,Spring Discovery,uBiome,Xbiome,XtalPi AME Cloud Ventures156Asimov,Atomwise,Auransa,BigHat Biosciences,BIOAGE LABS,Molecule.one F-Prime Capital66Adagene,BenchSci,Insilico Medicine,Notable,Neuromora Therapeutics,OwkinFelicis Ventures156BIOAGE LABS,Genesis Therapeutics,Juvena Therapeutics,LabGenius,ProteinQure,Spring DiscoveryTop-50 Investors in AI Companies45Deep Pharma IntelligenceINVESTORSINVESTMENTS OVERALLAI FOR DRUG DISCOVERY COMPANIES INVESTED IN Founders Fund106AbCellera Biologics,Datavant,Emerald Cloud Lab,Notable Labs,Roivant Sciences,DeepMind General Catalyst116Athelas,Beacon Biosignals,PathAI,Spring Discovery,Swoop,ThoughtSpot Obvious Ventures96ConcertoCare,Inato,LabGenius,Medable,Recursion Pharmaceuticals OrbiMed106AbCellera,Alector,Erasca,Insilico Medicine,Treeline Biosciences,XtalPi B Capital Group55Aetion,Atomwise,Insilico Medicine,Notable Labs,HiFiBiO DCVC Bio65Empirico,Frontier Medicines,Totus Medicines,Unlearn.AI,X-37 Lifeforce Capital75Clover Therapeutics,Notable Labs,PostEra,TARA Biosystems,Verge Genomics Lilly Asia Ventures85Gritstone Oncology,Hangzhou Just Biotherapeutics(Just China),Insilico Medicine,ShouTi,Transcenta Lux Capital115Alife,Auransa,LabGenius,Recursion Pharmaceuticals,Strateos New Enterprise Associates95Aetion,Black Diamond Therapeutics,Champions Oncology,Tempus,Vertex PharmaceuticalsTop-50 Investors in AI Companies46Deep Pharma IntelligenceINVESTORSINVESTMENTS OVERALLAI FOR DRUG DISCOVERY COMPANIES INVESTED INTencent75Atomwise,Brainomix,iCarbonX,PatSnap,XtalPiWuXi AppTec95 Arrakis Therapeutics,Verge Genomics,Schrdinger,Engine Biosciences,WuXi AppTec Baidu Ventures64Atomwise,Engine Biosciences,Kebotix,Insilico MedicineEDBI64Aetion,Biofourmis,Engine Biosciences,Erasca Novo Holdings84 Evotec,Exscientia,Kebotix,Tempus Third Rock Ventures74Celsius Therapeutics,Foundation Medicines,Insitro,TARA Biosystems Amadeus Capital Partners83Antidote.me,Healx,Quibim Amgen Ventures63 Aetion,Alector,GNS Healthcare 6 Dimensions Capital 53 Engine Biosciences,IDEAYA Biosciences,iTeos Therapeutics Baillie Gifford63Flatiron Health,Recursion Pharmaceuticals,TempusTop-50 Investors in AI Companies47Deep Pharma IntelligenceINVESTORSINVESTMENTS OVERALLAI FOR DRUG DISCOVERY COMPANIES INVESTED IN GT Healthcare Capital Partners63Exscientia,GT Apeiron Therapeutics,Ultromics Inovia Capital63BenchSci,LabGenius,ProteinQure OS Fund83Aria Pharmaceuticals,Arzeda,Emerald Cloud Lab Revolution63Amplion,NeuScience,Tempus Two Sigma Ventures83Exscientia,PathAI,Recursion PharmaceuticalsSequoia Capital Channel133Deep Intelligence Pharma,PathAI,XtalPi Bristol-Myers Squibb 52 Exscientia,PathAI EPIC Ventures62Recursion Pharmaceuticals,Unlearn.AI Celgene(BMS subsidiary)82Arrakis Therapeutics,GNS Healthcare Octopus Ventures62Antidote.me,eTherapeutics Big Pharmas Focus on AIDEEP PHARMA INTELLIGENCEAI and Pharma Collaborations in 2021-202249Deep Pharma IntelligenceJanFebMarAprMayJunJunJulMerck and Philips partner to advance AI-based personalized fertility treatment Roche Italia has joined forces with PatchAi to launch a virtual platform for cancer patients.Pfizers small-molecule programs will apply Iktos AI-driven de novo design software.AstraZeneca is teamed up with NVIDIA and the University of Florida on new AI research projects aimed at boosting drug discovery and patient care.Exscientia has signed up BMS for its AI-based drug discovery platform.The value of a deal could be as much as$1.2 billion.GSK announced an 18-month collaborative research agreement with AI company Progentec.Insilico Medicine entered into a collaboration with Teva to utilize Insilicos machine learning technology.Eli Lilly and Verge Genomics partnered to create new drugs for the treatment of amyotrophic lateral sclerosis using AI for drug developmentAI and Pharma Collaborations in 2021-202250Deep Pharma IntelligencePoolbeg Pharma launches AI programme with Eurofins Genomics.Japans Summit Pharmaceuticals International(SPI)partners with CytoReason to integrate its machine learning platform into the Japanese clinical drug discovery sector.DecJanJanJanFebRoche and Genentech will use Recursions platform for drug discovery in neurobiology and oncology fields Merck and AbSci partnered to produce enzymes using AbScis AI platform in a deal worth up to$610 million Exscientia collaborated with Sanofi to develop potential drug candidates for cancer and immune-mediated diseasesAmgen collaborated with Generate Biomedicines to create protein therapeutics for five clinical targets.Amgen will pay potentially up to$1.9 billion in this collaboration for a novel AI driven platform Bayer,Aalto and HUS expanded collaboration to apply artificial intelligence to support clinical drug trialsOctSep Insilico Medicine and Westlake Pharma announce cooperation relationship on accelerating the innovative drugs R&D for novel coronavirus.AugSelected Pharma AI Deals AI Companies Pharma CorporationsAI Companies 5151Deep Pharma IntelligencePharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.Selected Pharma AI Deals 5252Deep Pharma IntelligenceAI Companies Pharma CorporationsAI Companies Pharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.A Growing Number of Collaborations Involving AI for Drug DiscoverySummarizing industry observations over the last five years,we can observe a fundamental shift in perception of top executives at leading pharmaceutical organizations about the need of advanced AI technologies.Since 2015,there has been an obvious shift in the perception from skepticism and cuasious interest,all the way to a realization of a strategic role AI has to play in the emerging“data-centric”model of innovation.This change in perception was underpinned by a number of factors:a wave of proof-of-concept studies and research breakthroughs in a wide range of AI application use cases a number of commercial successes and successfully reached milestones,involving AI as a central element of research substantial advances in democratizing AI technology,where machine learning and deep learning algorithms become available at scale to non-AI experts decent increase in the overall understanding of AI“mechanics”,due to increasing efforts in the education and professional development with a focus on AI-driven tools and approachesPharmaceutical companies of all sizes start competing for AI-expertise,talent,and partnerships.In this report we summarize some of the most high-profile such collaborations,involving top-20 pharma giants.Even though,we can see a clear uprising trend in the number of collaborations,focused on AI-drug design,and other aspects of data mining and analytics.Deep Pharma Intelligence53The rising interest of leading pharma and contract research organizations towards AI-driven biotech startups is a major driver for the area to become more attractive for investors,since the industry is becoming well-suited for successful exit strategies in future.Increasing number of partnerships between Pharma and AI Companies over the last 6 yearsSources:Investment Digest AI in PharmaCorporation and AI-companies Participating in the Pharma AI DealsPharma PartnersAI and Biotech Partners54Deep Pharma IntelligenceTech PartnersThe leading Pharma players by the amount of major industry partnerships are AstraZeneca and Merck.These companies demonstrate increasing commitment to probing the grounds in the AI space by investing into internal programs,as well as partnering with external AI vendors to pilot programs in drug discovery and other research areas.The most common type of deals are true partnerships and saving the costs deals.The leading big pharma brands are increasingly open to partnerships with AI startups and corporations to getcompetitive edge,and mitigate theproblem of declining R&D efficiency.55Deep Pharma IntelligenceLeading Pharma Corporations by the Number of Pharma AI Deals in 2021-Q1 202256Deep Pharma IntelligenceThe leading AI players by the amount of major industry partnerships are Insilico Medicine and Atomwise.The biggest number of AI in Drug Discovery deals was conducted by Insilico Medicine.The company is an end-to-end,AI-driven pharma-technology company that accelerates drug development by proprietary platform across biology,chemistry and clinical development.All of the deals concluded with this company were categorized as the ones aiming at saving costs and increasing operational efficiency due to thecharacter of the services provided.Top-10 AI and Tech Partners by Number of Major Pharma AI Deals in 2021-Q1 2022DEEP PHARMA INTELLIGENCEAI in Pharma Publicly Traded CompaniesAI in Pharma Publicly Traded Companies Deep Pharma Intelligence58ConnecticutNord-Pas-de-CalaisGENFIT(GNFT)Loos,Nord-Pas-de-Calais,FranceBioXcel Therapeutics(BX2)Branford,Connecticut,USMassachusettsColoradoTexas&UtahRelay Therapeutics(RLAY)Cambridge,Massachusetts,USITeos Therapeutics(ITOS)Cambridge,Massachusetts,USBiodesix(BDSX)Broomfield,Colorado,USEvolutionary Genomics(FNAM)Lafayette,Colorado,USLantern Pharma(LTRN)Dallas,Texas,USRecursion Pharmaceuticals(RXRX)Salt Lake City,Utah,USNew YorkSchrdinger(SDGR)New York,New York,USCaliforniaAlector(ALEC)San Francisco,California,USBiomea Fusion(BMEA)Redwood City,California,USBerkeley Lights(BLI)Emeryville,California,USGritstone Oncology(GRTS)Emeryville,California,USIDEAYA Biosciences(IDYA)San Francisco,California,USSangamo Therapeutics(SGMO)Richmond,California,USHong-KongRegent Pacific GroupHong-KongDeep LongevityAcquired for$4M by Regent Pacific GroupLondonManchesterGlasgowCotinga Pharmaceuticals(COTQF)London,England,UKC4X discovery(C4XD.L)Manchester,Manchester,UKDeepMatter Group(DMTR.L)Glasgow,Glasgow City,UKOxfordshireSensyne Health(SENS.L)Headington,Oxfordshire,UKeTherapeutics(ETX.L)Hanborough,Oxfordshire,UKRenalytix AI(RENX)Penarth,Oxfordshire,UKSources:Investment Digest AI in PharmaAI in Pharma Publicly Traded CompaniesDespite the crisis and high volatility,AI-in-Pharma publicly traded companies present growth reaching$110B of cumulative capitalization as of December 31,2021.A huge leap in comparison with the capitalization presented in AI-in-Pharma Investment Digest Q3 2021 occurred after adding Vertex Pharmaceuticals and Astellas Pharma companies to the list.Two companies from our list have announced closing of IPO in Q4 2021:Exscientia and Roivant Sciences.The largest companies by market capitalization are Recursion,Exscientia and Relay Therapeutics.Its essential to measure the performance of publicly traded AI in Pharma companies via comparison with major market benchmarks such as IBB,NBI and S&P 500.The cumulative market capitalization dynamics of AI in Pharma corporations outperformed YTD NASDAQ Biotechnology Index(NBI)and iShares Biotechnology ETF(IBB).However,S&P 500 gained solid 28.79%which placed it at the first place.59Deep Pharma IntelligenceCumulative Capitalization of Publicly Traded AI-in-Pharma Discovery Companies,2020-2021,$BillionMarket Capitalization Growth During 2021Sources:Investment Digest AI in PharmaTop-10 AI-Driven Publicly Traded Pharma Companies by Market Capitalization in 202160Deep Pharma IntelligenceSources:Investment Digest AI in PharmaThe chart presents the Top-10 AI-driven drug discovery public companies arranged by market capitalization as of end of December 2021.Relay Therapeutics,the developer of an allosteric drug-discovery platform intended to apply computational techniques to protein motion holds the first place with$3.3B of market capitalization.AI in Pharma IPOs in 202161Deep Pharma IntelligenceIn 2021 new public companies have successfully closed their IPOs.As for now,almost all these companies are showing a slight decline,which is typical for new pharmaceutical companies,especially with the negative net income.All IPOs took place in the USA.All companies have beta smaller than 1(although positive),which means that AI in pharma stock prices move following the general market,yet the degree of such“movements”is lower.Major adverse market events in 2020-2022 did not significantly affect AI in pharma sector.The industrys features remain to play a designative role in the overall market volatility.NameCountryFunding Amount,$MIPO DateCapitalization,$MValuation at IPO,$MIPO Share Price,$Current Share Price,$EV/EBITDANet Income,$MEvaxion BiotechUSA3004.02.2164.5648910.003.03-2.61-24.53Biomea FusionUSA15315.04.21157.5246317.004.720.99-41.57Recursion PharmaceuticalsUSA465.416.04.211170274818.007.80-4.07-186.48ErascaUSA30016.07.211350180016.908.65-6.60-122.76SOPHiA GENETICSUSA250.222.07.2155223418.457.66-3.43-73.68ExscientiaUK474.401.10.2124702.92214.32-15.09-66.44Roivant SciencesUSA210001.10.2136107.39.354.54N/A-1.060Sources:Investment Digest AI in PharmaFundamental Analysis of AI in Pharma Public Companies Compared to its peers,EVAXION has the lowest market capitalization and Enterprise Value making Evaxion cash burden is less than its peers.Evaxion market capitalization is continuously increasing due to the expectation of advancing clinical trials.As of June 2021,Evaxions cash position of$18.8 million is expected to be sufficient to fund key clinical programs into 2022.Due to the U.S.Food and Drug Administration(FDA)having cleared the companys Investigational New Drug application to begin a Phase I trial of BMF-219,Biome draws attention from investors.The company has expanded team and in-house research capabilities to support long-term growth and clinical and preclinical development plans.As of June 30,2021,the Company had cash,cash equivalents,restricted cash,and investments of$203.0 million.The clinical developments of the company should enhance it financial positions.Compared to its peers,Recursion Pharma is the one that has a huge revenue growth with 120%in 2021(LTM).The 74%revenue growth in 2020 makes the companys market position even better.One of the reasons that the company wasnt able to reach positive EBITDA is that the company expanded the total number of research and development programs from 37 to 48 as well as its operations to Canada.As of June 30,2021,Recursions cash and cash equivalents were$632.7 million.62Deep Pharma IntelligenceSources:Investment Digest AI in PharmaFundamental Analysis of AI in Pharma Public Companies Berkeleys revenue grew to$77.8 million for 2021(LTM),representing a 21%growth.Berkeley Lights continues to expect full year 2021 revenue to be in the range of$90 million to$100 million,representing 40%to 56%growth over the full year of 2020.Berkeley kept gross margin above 65%in the last 3 year.On the contrary of revenue growth,we saw that Berkeleys market capitalization is decreasing significantly with-57%.The main reason for the decrease in market cap is that the company gave guidance for 2021 in the range of growth between 40%and 56%over the prior year.From investors point of view BLIs growth is about to stumble.Relays revenue goes steady with expectation of 2%growth in 2021.Relay has acquired ZebiAI in April 2021 which affected$134.9 million in expenses.Despite the acquisition,the company is projected to reach 50%of the gross margin in 2021,continuing its great performance from 2020,when the margin reached 100%.As institutional investors increased their shares in Relay,companys market capitalization increased 3%in 2021(LTM).As of June 30,2021,cash,cash equivalents and investments totaled approximately$671.2 million.The Company expects its current cash and cash equivalents will be sufficient to fund its current operating plan into 2024.63Deep Pharma IntelligenceSources:Investment Digest AI in PharmaFundamental Analysis of AI in Pharma Public Companies Total Revenue is$120 million in 2021(LTM),expected an increase of over 12%compared to 2020.Gross profit is expected to reach over$62 million in 2021 with a gross margin over 62%.The companys expenses are projected to scale due the development of its internal drug discovery programs.Operating expenses reached$42.3 million in Q2-2021,compared to$30.7 million in Q2-2020.Although Schrdinger is expected to maintain its revenue growth rate,its definitely expected to grow faster than the wider industry.The companys total revenue for the second quarter of 2021 was$10.2 million compared to$5.9 million for the second quarter of 2020,representing a 72%increase.This increase was primarily driven by new customers onboarding onto the platform.Another reason is the usage rates improvement across existing customers.SOPHiA Genetics full year revenue for 2021 expects to be greater than$39 million,representing growth of over 37%compared to the prior year period.The decline in gross margin was primarily attributable to increased computational and storage-related costs and negative FX movement.However,the company kept its margin above 60%in the 3 years.The company has successfully closed its$345 million upsized IPO in July 2021.A few reasons that the company has a$2.3 billion market capitalization:1.Nominated ERAS-3490 Development Candidate;2.Dosed First Patient in HERKULES-1 Study;3.Dosed First Patient in FLAGSHP-1 Study;4.Executive Leadership Team.64Deep Pharma IntelligenceSources:Investment Digest AI in PharmaEvaxion Biotech65Deep Pharma IntelligenceTickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$MEVAX-0.42%7.35%-73.20e.56MEVAXs stock price has been approaching peak growth thanks to newly-created Chief Scientific Officer role strengthens Evaxions leadership team.Evaxion Biotech is devoted to the discovery and development of vaccines against cancer and infectious diseases.IT is a clinical-stage AI-immunology platform company decoding the human immune system to discover and develop novel immunotherapies to treat cancer and infectious diseases.The graph below depicts a comparative performance of EVAX and 3 ETFs starting from 15.03.2021:Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF(IPO).Sources:Investment Digest AI in PharmaBiomea Fusion66Deep Pharma IntelligenceOwing to its IPO,Biomea held unrestricted cash and short-term investments of$198.6 million and no debt.The company is on pace to burn only$30 million in 2021.Biomea Fusion is a precision oncology company developing novel small molecules that target aggressive forms of cancer.Biomea Fusion has a development portfolio that targets specific gene alterations which occur in the DNA of patients that translate into key drivers of tumor growth.The graph below depicts a comparative performance of BMEA and 3 ETFs starting from 16.04.2021:Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF(IPO).TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$MBMEA-0.5%5.01%-50.767.52MSources:Investment Digest AI in PharmaRecursion Pharmaceuticals67Deep Pharma IntelligenceRecursion Pharmaceuticals operates as a clinical-stage biotechnology company decoding biology by integrating technological innovations across biology,chemistry,automation,data science,and engineering to industrialize drug discovery.The graph below depicts a comparative performance of RXRX and 3 ETFs starting from 16.04.2021:Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF(IPO).The Institutional investors have recently added to their stakes in the company.Rockefeller Capital Management L.P and Citigroup Inc are among them.TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$BRXRX-0.68%5.88%-70.67%1.17BSources:Investment Digest AI in PharmaSOPHiA GENETICS68Deep Pharma IntelligenceSOPHiA GENETICS is a healthcare technology company dedicated to establishing the practice of data-driven medicine as the standard of care and for life sciences research.It is the creator of a cloud-based SaaS platform capable of analyzing data and generating insights from complex multimodal data sets and different diagnostic modalities.The graph below depicts a comparative performance of SOPH and 3 ETFs starting from 23.07.2021:Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF.SOPH is operating in a growing industry,but the proposed IPO terms looked pricey for analysts.Nevertheless,the stock price has risen since then.TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$BSOPH-0.41%4.91%-54.97%0.552BSources:Investment Digest AI in PharmaERASCA69Deep Pharma IntelligenceErasca develops oncology drugs intended to provide precision oncology options.The companys drugs are being developed through multiple discovery programs for undisclosed targets that are biological drivers of cancer and are pursuing additional opportunities for pipeline expansion through academic and biopharmaceutical collaborations,providing patients with new potential solutions to not just treat but cure cancer.The graph below depicts a comparative performance of SOPH and 3 ETFs starting from 16.07.2021:Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF.Fierce Biotech has named it as one of 2021s“Fierce 15”biotechnology companies,identifying Erasca as one of the industrys most promising biotechnology companies.TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$BERAS-0.26%4.5%-48.39%1.35BSources:Investment Digest AI in PharmaExscientiaFounded in 2012,Exscientia was the first company to automate drug design and have an AI-Designed molecule enter clinical trials,as a full stack AI drug discovery company.EXAI brought the first drug developed entirely by AI into clinical trials last year and has received a tremendous amount of interest in recent months.In fact,EXAI leveraged this success into two back-to-back VC rounds.EXAI just raised$109M in its Series C round back in March.And it followed that up with a$225M Series D round in April less than two months later.Estimation of monthly return by FF5F model in November showed that EXAI is overpriced by market and its monthly return reduces during December.The graph below depicts a comparative performance of EXAI and 3 ETFs in Q1 2022(starting from 01.10.2021).70TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$BEXAI-0.49%6.51%-42.96%2.47BDeep Pharma IntelligenceSources:Investment Digest AI in PharmaRoivant SciencesROIV awaits potential approval by the U.S.Food and Drug Administration(FDA)for Dermavants tapinarof in treating plaque psoriasis and has experimental drug in a phase 3 study for treating atopic dermatitis.Founded in 2014,Roivant Sciences develops transformative medicines faster by building technologies and developing talent in creative ways,leveraging the Roivant platform to launch Vants-nimble and focused biopharmaceutical and health technology companies.Based on estimation ROIV underpriced so after decline in Nov,stock price start increasing on the last month of 2021.The graph below depicts a comparative performance of ROIV and 3 ETFs in Q1 2022(starting from 15.03.2021):Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF(IPO).71TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$BROIV-0.26%4.35%-46.73%3.61BDeep Pharma IntelligenceSources:Investment Digest AI in PharmaTop AI in Pharma Best-Promising Companies in 2021Schrdinger,Berkeley Lights and Relay Therapeutics constitute the group of promising companies selected for analysis.They are new to the market(their IPOs closed in 2020).Therefore,their future might change significantly.Moreover,they have decent multi-target pipelines of novel therapeutics to address unmet medical needs.The companies are expected to translate their proprietary insights and technical solutions into effective therapeutics.Currently,the companies have a firm market position and thus receive high expectations from investors.NameCountryFunding Amount,$MIPO DateCapitalization,$BValuation at IPO,$MIPO Share Price,$Current Share Price,$EV/EBITDANet Income,$MSchrdingerUSA562.302.05.20202.3681917.0035.90-19-100.393Berkeley LightsUSA208.517.07.20200.4661355.219.007.34-5.48-71.72Relay TherapeuticsUSA520.016.07.20202.76173620.0034.88-11.49-363.87272Deep Pharma IntelligenceSources:Investment Digest AI in PharmaSchrdinger73Deep Pharma IntelligenceAccording to the analyst,compared to the current share price,the company appears a touch undervalued at a over 20%discount to where the stock price trades currently.Schrdingers industry-leading computational platform facilitates the research efforts of biopharmaceutical and industrial companies,academic institutions,and government laboratories worldwide.Schrdinger also has wholly-owned and collaborative drug discovery programs in a broad range of therapeutic areas.The graph below depicts a comparative performance of SDGR and 3 ETFs:Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF(IPO).TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$BSDGR-0.37%3.68%8.41%2.36BSources:Investment Digest AI in PharmaBerkeley Lights74Deep Pharma IntelligenceBerkeley Lights is a leading Digital Cell Biology company focused on enabling and accelerating the rapid development and commercialization of biotherapeutics and other cell-based products for the customers.The Berkeley Lights Platform captures deep phenotypic,functional and genotypic information for thousands of single cells in parallel.The graph below depicts a comparative performance of RXRX and 3 ETFs starting from 15.03.2021:Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF(IPO).Shares of Berkeley Lights have been surging consistently after revenue results in Q3-2021.The company reported its largest revenue placement estimated between the range of$24m to 24.3m a 33%growth year-over-year.TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$BBLI-0.84%5.65%-90.42%0.46BSources:Investment Digest AI in PharmaRelay Therapeutics75Deep Pharma IntelligenceRelay Therapeutics is a company focused on precision oncology and rare genetic diseases.Their proprietary Dynamo platform puts protein motion,at the heart of the drug discovery process.It uses advanced machine learning to identify potential novel target binding sites and to predict and design potentially the most effective molecules.The graph below depicts a comparative performance of RXRX and 3 ETFs starting from 15.03.2021:Vanguard Health Care Index Fund ETF(VHT),iShares Nasdaq Biotechnology ETF(IBB),Renaissance IPO ETF(IPO).93.9%of RLAY shares are owned by institutional investors which indicates endowments,large money managers and hedge funds believe a stock will outperform the market.TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$BRLAY-0.2%4.07%-32.81%2.76BSources:Investment Digest AI in PharmaAI in Pharma Corporations Financials76Deep Pharma IntelligenceAI in Pharma corporations tend to be more volatile than average publicly traded company.For most of the corporations,daily returns are positive and abnormal compared to the market.More volatile stocks are usually characterized by higher betas(both calculated for IBB index and for S&P 500).AI in Pharma segment is definitely a segment of growth stocks with the investors focused on the prospects of the companies rather than on the dividends.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB BetaS&P 500 BetaTotal Funding Amount,$MOperating MarginEV/EBITDANet Income,$MGritstone Oncology394-0.20%5.58%-3.15%-13.10%0.379396-155.99%-1.85-75.08Lantern Pharma77.54-0.31%4.26%5.48#.81%0.701.3268.700.00%-0.58-12.36Alector10740.00%5.71%4.85%-9.49%1.151.34194.50-18.04%-16.55-36.33Relay Therapeutics32920.02%4.19%5.67D.35%N/A1.34520.00-7 464%-12.74-363.87Schrdinger2235-0.28%3.67%-12.47%6.94%0.981.14567.20-80.80%-18.91-100.39Sensyne Health 5.20-7.49%9.00%2.75%-33.55%0.680.8737.25-450.76%-0.41-34.83Berkeley Lights412-0.65%5.44.26%7.09%1.37N/A272.60-82.90%-5.83-71.72LargeMediumLowSources:Investment Digest AI in PharmaAI in Pharma Corporations FinancialsDeep Pharma Intelligence77Market capitalization of some AI in Pharma corporations(such as Schrdinger)exceeds$6B whereas other companies are priced in the range of dozens of millions of dollars-the difference in the valuation is immense.There is no strong correlation between operating margin or net income and market capitalization-the valuation of the corporations still being unprofitable can exceed billion of dollars.Selling shares to investors allows them to maintain their cash burn ratios on an acceptable levels.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB BetaS&P 500 BetaTotal Funding Amount,$MOperating MarginEV/EBITDANet Income,$MBiodesix55.08-0.79%5.54%-10.85%-17.89%1.291.43289.70-72.14%-0.98-43.16C4X discovery94.93-0.05%3.21%8.74.69%0.140.188.71-104.45%-8.84-3.844DeepMatter Group8.41-0.75%6.32%-5.76%-11.63%1.080.37N/A-216.97%-2.60-2.621eTherapeutics183.350.18%4.28.72!.17%0.110.9798.50-640.16%-19.97-4.122GenFit198.880.03%4.51.68$.84%1.240.8393.69-1048.54%-5.61-39.152Biomea Fusion132.77-0.45%4.91%-14.26%-28.59%0.530.3256.000.00%0.89-41.57Sources:Investment Digest AI in PharmaLargeMediumLowAI in Pharma Corporations FinancialsDeep Pharma Intelligence78Market capitalization growth of AI-driven Pharma corporations exceeds that of the entire market and general BioTech Industry indices represented as S&P 500 index and IBB,respectively.The difference is that compared to the general market,the AI-driven pharma market segment is more volatile.The distribution of the returns in the segment of AI-driven pharma companies is right-skewed,which differentiates it from the vast majority of stock indices and segments.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB BetaS&P 500 BetaTotal Funding Amount,$MOperating MarginEV/EBITDANet Income,$MBioXcel Therapeutics466.57-0.17%4.51%-5.89%-0.17%1.041.03N/A0.00%-3.05-106.93Evolutionary Genomics4.410.59.97%-5.89%-11.76%-0.45-0.071.50.00%-5.15-2.794IDEAYA Biosciences436.49-0.22%3.87%2.48%-7.14%1.791.47226.10-179.91%-4.41-49.760ITeos Therapeutics12470.08%4.14%-5.47%-1.68%1.090.73249.7471.03%1.50214.52Recursion Pharmaceuticals1136-0.46%5.70%-1.16%-5.64%1.481.22465.38-1795.78%-4.06-186.479Sangamo Therapeutics834.524-0.20%3.33%7.98%5.40%1.501.1493.20-165.62%-3.51-178.286Renalytix AI231.687-0.45%4.05%1.66%-20.00%1.471.0576.40-1971.62%-3.85-42.402Evaxion Biotech68.45 0.03%8.06%7.58%4.69%0.900.9617.000.00-1.69-24.53Sources:Investment Digest AI in PharmaLargeMediumLowTop Publicly Traded Companies Related to AI-PharmaDEEP PHARMA INTELLIGENCECompanies Related to AI-PharmaDeep Pharma Intelligence80AI in pharma sector is an integral part of the contemporary pharmaceutical industry.AI-Pharma sector,defined broadly,is not limited to AI companies,but includes also pharma,tech,chemistry corporations,and CROs that are engaged in collaborations with AI startups,including but not limited to:Mergers&Acquisitions,scientific researches,partnerships,and so on.Hence the companies chosen are better to be described as AI-related or AI-aiming than AI-based solely.The number of new partnerships between pharma companies and AI companies is ever increasing across the whole industry.On the one hand,AI-focused companies may spend a few years developing all software and tools which pharma companies do not have.On the other hand,large companies,mainly public ones,have solid understanding of their science,extensive experience in the industry and regulatory field,and they are ready to share the risk.In this chapter we introduce the list of top corporations related to AI-Pharma that were selected based on the analysis of their R&D,financials,and collaborations with the most promising and advanced AI-Pharma startups.Big Pharma CompaniesAI CompaniesBiotechnology CompaniesData Integration CompaniesGenetics CompaniesAI in PharmaPublicly Traded Companies Related to AI-PharmaDriven to some extent by the COVID-19 pandemic,publicly traded companies related to AI-Pharma demonstrated significant growth,reaching$5.13T industry capitalization as of the end of Q1 2022.Investors interest is being shifted towards industries of this nature.We see significant potential for Artificial Intelligence in the Pharmaceutical Industry.The Expected Compound Annual Growth Rate for this is market is projected to be around 40%over the next 3 years.The Biotechnology Industry is poised to witness a quantum leap soon,mainly because of the impact of Artificial Intelligence on biomedicine R&D.Many transactions are being announced,including Parexels acquisition for$8.5B,that indicates growing awareness of the disruptive potential in this sector for ones having the right means for participation.COVID-19 will continue to push valuations and M&A activity in the sector.Deep Pharma Intelligence81Cumulative Capitalization of Publicly Traded Companies Related to AI-Pharma,2021-2022,$TrillionSources:Investment Digest AI in PharmaTop 10 Publicly Traded AI-Pharma Related Companies by Market Capitalization in 2022The chart represents the top-10 public companies that ended up in our portfolios according to their market capitalization.Johnson and Johnson,United Health Group and NVIDIA top our list,accounting 50.5%of the capitalization of all companies included.Despite the performance decline that Vertex Pharmaceuticals have had in the past year,it still ended up at our top.During the last year and a half period of pandemic,AstraZeneca has being raised the capitalization by more than 11 times,reaching$181B.82Deep Pharma IntelligenceSources:Investment Digest AI in PharmaRoche Holding(RHHBY)Roche Holding AG offers pharmaceutical products for treating anemia,cancer,cardiovascular,central nervous system,dermatology,hepatitis B and C,HIV/AIDS,inflammatory,autoimmune and other diseases.The company widely implements data-driven solutions,for example Roche has acquired Viewics,Inc.Viewics focuses on business analytics for laboratories,taking data from a variety of sources and extracting it to make faster data-driven decisions in operating processes in the labs.Novo Nordisk(NVO)Novo Nordisk is a healthcare company,engages in the research,development,manufacture,and marketing of pharmaceutical products worldwide.It operates in two segments,Diabetes and Obesity care,and Biopharm.Novo Nordisk actively implements different AI in Pharma solutions,its foundation awards DKK 138 million under its new data science and artificial intelligence initiative.Astrazeneca(AZN)Astrazeneca discovers,develops,manufactures,and commercializes prescription medicines in the areas of oncology,cardiovascular,renal and metabolism,respiratory,infection,neuroscience,and gastroenterology worldwide.Astrazeneca uses advancing genomics research with AI and big data,AI is already being embedded across companies R&D both for research and experiment optimization.AbbVie(ABBV)AbbVie is one of the so-called Big Pharma companies.The company uses AI not only for direct development but also for its own enhancement:Abbelfish Machine Translation and AbbVie Search are built for accelerating and scaling the work of the company researchers,reducing the time it takes to discover and deliver transformative medicines and therapies for patients.Top Publicly Traded Companies Related to AI-Pharma83Deep Pharma IntelligenceBerkeley Lights(BLI)Berkeley Lights is a leading Digital Cell Biology company focused on enabling and accelerating the rapid development and commercialization of biotherapeutics and other cell-based products for the customers.Besides 2 unique optofluidics system,Berkeley Lights is known for antibody discovery and cell lines development that definitely requires the usage of AI-powered algorithms and technical solutions.DeepMatter Group(DMTR)DeepMatter Group Plc operates as a big data and analysis company.It offers DigitalGlassware platform to deliver applications resulting in optimized chemicals,materials,and formulations in various areas,such as pharmaceutical research,fine chemicals,scientific publications,and teaching.The company develops and commercialises cheminformatics software to handle,store,and retrieve chemical structures and reactions for application in pharma;and tools for the production of synthesis planning and reaction prediction solutions,as well as engages in the automatic extraction of scientific information from text and images.Pharmaceutical Product Development(PPD)Pharmaceutical Product Development is another big CRO company.PPD ended up in our portfolio for a great reason,collaborating with Happy Life Tech for AI support,the company aims to create Data Science-driven Clinical Research Solutions in China to enhance global drug development.Charles River Laboratories(CRL)Charles River Laboratories is a well-known Contract Research Organization(CRO)specializing in research and drug development.CRL uses the AtomNet platform,which is a deep convolutional neural network created for structure-based drug discovery.The company also works with the Valence Discovery Platform for Hit-to-Lead acceleration and optimization and provides all research services considering these platforms.Top Publicly Traded Companies Related to AI-Pharma84Deep Pharma IntelligenceAgilent(A)Agilent is one of the biggest Biotech companies providing technical solutions for the Pharmaceutical industry.Lots of company technical solutions already have built-in or support different type of AI algorithms.Also,Agilent and Visiopharm co-promote advanced digital Precision Pathology Solutions.Thermo Fisher Scientific(TMO)Thermo Fisher is another,even bigger,Biotech company that is specializing in technical solutions,providing also a wide range of other services.“The connected Lab”is a good example of AI-enhanced services providing by the company,creating solutions for enhanced in-Lab performance via AI-based info-gathering and analysis.AI-based processing tools are now also available in Thermo Scientific Amira-Avizo Software and PerGeos Software.Johnson and Johnson(JNJ)Johnson and Johnson is considered o be among the TOP-3 biggest Pharmaceutical companies in the world,therefore not only implementation but also investing in AI in Pharma is provided by the company.In 2020,J&J announced an investment in Datavant Holdings,which is working to help healthcare organizations unite data across institutions to enhance medical research and patient care.Another JJI partner,Aetion Inc.,analyzes electronic medical records,insurance claims,patient registries and lab results to generate healthcare-related decisions.Almirall(ALM)Almirall is a leading skin-health focused global pharmaceutical company,that has some recent collaborations with Iktos for the creation of generative modelling AI technology for quick identification of molecules with multiple bioactivity and drug-like criteria.Top Publicly Traded Companies Related to AI-Pharma85Deep Pharma IntelligencePerformance of Optimized Portfolio 86Deep Pharma IntelligenceInvestTech Advanced Solutions analytics used four different strategies during the creation of portfolios:Equally Weighted(Balanced),Minimum Variance,Capitalization-Weighted,Sharpe Ratio-based.Sources:Investment Digest AI in PharmaAs a result of our analysis,50000 portfolios were simulated where obtained optimized ones.Capitalization-Weighted Portfolio showed performance in 22.37%of Cumulative Return against Benchmark NBI(Nasdaq Biotechnology Index)over shown the period.MetricStrategyBenchmarkCAGR.98%-15.56%Sortino1.82-0.87Kurtosis1.410.1Conclusions87Deep Pharma IntelligenceThe AI in Pharma sector have been experienced significant growth over the past year.This could be clearly seen by the overall dynamics of the chosen companies.Despite common misconception,AI in Pharma includes not only SaaS-specialized companies,Big Pharma,as well as top-CRO companies should be included in this list among other newcomers.Due to the markets significant growth,all portfolios shows remarkable results,indicating valid investment opportunities.At the same time such opportunities,i.e.possibilities,shouldnt be replaced with certainty.There are plenty of companies that should be considered as promising ones,which shows both market dynamics and investment expediency.Best performance is shown by Sharpe ratio-based portfolios,our Balanced portfolios have outperformed all classical Capitalization-based portfolios showing higher investment expediency.The overall performance of different investment waging strategies shows that all types of investors,both risk averse and risk-seeking,will find enough opportunities for themselves.87Sources:Investment Digest AI in PharmaDeep Pharma Intelligence:Upcoming Projects and Analytical Tools88Deep Pharma IntelligenceDeep Pharma Publicly Traded Companies Big Data Analytics DashboardAI for Advanced R&D:Applications and Use CasesDEEP PHARMA INTELLIGENCENotable AI Breakthroughs90Deep Pharma IntelligenceIBM Watson released a cognitive computing platform for Clinical trial matching that has shown significant improvement in patient enrollment rate at Mayo Clinic.The platform demonstrated an 80%increase in enrollment in clinical trials for breast cancer and a decrease in time to match a clinical trial to one patient.Healx has prepared a rare disease Fragile X syndrome drug for a Phase 2a clinical trial in 15 months.Healx has demonstrated the power of combining domain expertise,deep learning,and proprietary data.DeepMind built the AlphaFold platform to predict 3D protein structures that outperformed all other algorithms.AlphaFold won the CASP13 competition,where it could most accurately predict the shape for 25 of the 43 proteins without using previously solved proteins as templates.Recursion Pharmaceuticals has evaluated Takedas preclinical and clinical molecules in over 60 indications in less than 18 months by Recursions AI-enabled drug discovery platform.Insilico Medicine has published a research paper about the first in vivo active drug candidate developed from scratch(de-novo)in 46 days(with target selection)using the GENTRL AI-based system.Oct 2018Mar 2018Dec 2018Sep 2019Jan 2019Notable AI Breakthroughs91Deep Pharma IntelligenceDeep Genomics created a DG12P1 drug in 18 months using an AI-augmented drug design.It is an antisense oligonucleotide therapy to treat rare Wilson disease.Deed Genomics platform screened over 2,400 diseases and over 100,000 mutations to predict and confirm the precise disease-causing mechanism of the mutation Met645Arg.Mendel Recruit proprietary platform increases patient enrollment for clinical trials by 24-50%.It applies AI algorithms that combine the recognition of scanned documents with natural language processing of clinical records and automated clinical reasoning.A new drug candidate,DSP-1181,created using the Exscientia Centaur Chemist Artificial Intelligence platform,began clinical study.The drug was developed together with Sumitomo Dainippon Pharma for the treatment of an obsessive-compulsive disorder.It was advanced to Phase 1 clinical trials.Scientists from MIT discovered halicin a new super powerful antibiotic capable of killing 35 of the worlds most problematic disease-causing bacteria,including multiresistant strains.The model applied was able to screen more than a hundred million chemical compounds and pick out potential antibiotics that kill bacteria using different mechanisms than existing drugs.Aladdin has built a platform for the early diagnostics of Alzheimers disease and COVID-19.Disease Diagnosis platform uses AI and multimodal data,including biomarkers,imaging,blood samples,medical records,etc.Jan 2020Sep 2020Sep 2019Feb 2020Jan 2020Notable AI Breakthroughs92Deep Pharma IntelligenceMELLODDY the Machine Learning Ledger Orchestration for Drug Discovery group was created by ten pharma companies to develop ML models without sharing data.MELLODDY leverages the worlds most extensive collection of small molecules with known biochemical or cellular activity to provide more accurate predictive models and improve drug discovery efficiency.Insilico Medicine achieved industry-first nominating Preclinical Candidate.The company performed all the required human patient cell,tissue,and animal validation experiments to claim a first-in-class preclinical candidate for a novel pan-fibrotic target.The company is preparing for clinical development.Cyclica launched an AI-based drug discovery platform that achieved over 60%of actionable hits for its pharma clients.Cyclica has partnered with over 100 global pharma and biotech companies and academia across many therapy areas.These partnerships have resulted in 64%of programmes resulting in actionable hits.BioXcel Therapeutics,Inc.,a clinical-stage biopharmaceutical company utilizing AI approaches,announced that the FDA has accepted for filing the New Drug Application for BXCL501,for the acute treatment of agitation associated with schizophrenia and bipolar disorders I and II.Using its AI technology,Exscientia designed an Alzheimers disease drug candidate who has entered Phase I clinical testing.The AI-designed drug candidate will be assessed for improved antipsychotic effects associated with Alzheimers psychosis,in addition to improvements in behavioural and psychological symptoms of dementiaSep 2020Mar 2021May 2021Feb 2021May 2021Notable AI Breakthroughs93Deep Pharma IntelligenceThe University of Washington has developed a deep learning model,“RoseTTAFold”,that calculates protein structure on a single gaming computer within ten minutes.Insilico Medicine announces the preclinical candidate for kidney fibrosis discovered using end-to-end Artificial Intelligence engine.The preclinical candidate has the desirable pharmacological properties,pharmacokinetic profile and demonstrated auspicious results in in-vitro and in-vivo preclinical studies.Exscientia,in cooperation with the Medical University of Vienna,published a paper that illustrates the potential real-world impact of using Exscientias AI-supported precision medicine platform.The platform proposes the most effective therapy for late-stage haematological cancer patients based on testing drug responses ex vivo in their own tissue samples.AstraZeneca,Merck,Pfizer and Teva formed AION Labs,the innovative lab that will create and adopt AI technology to transform the process of drug discovery.AION Labs will create and invest in companies that implement AI for drug development.Additionally,they will offer special resources and mentorships to such companies.The AI-empowered company Healx has secured FDA approval for a phase 2a clinical trial of an AI-discovered compound that could help manage the symptoms of the genetic disorder Fragile X syndrome.Jul 2021Oct 2021Oct 2021Oct 2021Aug 2021Standigm had established a Synthetic Research Center in the headquarters of SK Chemicals Co.,Ltd(SK Chemicals,KRX 285130),a life science and green chemicals company.Notable AI Breakthroughs94Deep Pharma IntelligenceInsilico Medicine,an end-to-end artificial intelligence(AI)-driven drug discovery company,announced that the first healthy volunteer has been dosed in a first-in-human microdose trial of ISM001-055.BenevolentAI,a leading clinical-stage AI drug discovery company,announced that AstraZeneca had added a novel target for idiopathic pulmonary fibrosis(IPF),discovered using BenevolentAIs platform,to its drug development portfolio.This is the second novel target from the collaboration that has been identified,validated,and selected for AstraZenecas portfolio.Lantern Pharma presented positive data on the effectiveness of LP-284 in hematologic cancers at the 63rd American Society of Hematology(ASH)Annual Meeting.Erasca announced the FDA has cleared an investigational new drug application for ERAS-801,an orally available small molecule epidermal growth factor receptor inhibitor specifically designed to have high central nervous system penetration for the treatment of recurrent glioblastoma multiforme.Dec 2021Dec 2021Dec 2021Nov 2021Nov 2021Notable AI Breakthroughs95Deep Pharma IntelligenceAbCellera and its collaborators released new preclinical data showing the pseudovirus neutralization status of its two monoclonal antibodies,bamlanivimab and bebtelovimab(also known as LY-CoV1404),against the Omicron variant.Bristol Myers Squibb announced the CMPH of the EMA has recommended approval of Breyanzi,a CD19-directed chimeric antigen receptor T cell therapy for the treatment of adult patients with relapsed or refractory(R/R)diffuse large B-cell lymphoma(DLBCL),primary mediastinal large B-cell lymphoma(PMBCL),and follicular lymphoma grade 3B(FL3B)after two or more lines of systemic therapy.AI Therapeutics announced the initiation of a Phase II study for a promising new approach to treat amyotrophic lateral sclerosis(ALS).Aizon announced the launch of its new asset monitoring application for pharmaceutical manufacturers and biotech companies.Built on Aizons GxP compliant AI SaaS Platform,Aizon Asset Health provides intelligent historical maintenance analysis,proactively monitors the condition of critical assets in real time,and provides actionable maintenance recommendations that keep equipment up and running optimally.Cyclica launched Perturba Therapeutics-a spin out from the Stagljar Lab at the University of Toronto,Donnelly Centre for Cellular and Biomolecular Research.Perturba is advancing a rich pipeline of assets from undrugged protein-protein interactions.Feb 2022Feb 2022Feb 2022Jan 2022Jan 2022DEEP PHARMA INTELLIGENCEComputational Methods Used by the Most Advanced AI CompaniesComputational Methods Used by the Most Advanced AI Companies97Deep Pharma IntelligenceNatural Language ProcessingComputational MethodsMachine LearningDeep LearningChemoinformaticsBioinformaticsSymbolic AIReinforcement LearningGANsQuantum ComputingCNNEvolutionary AlgorithmsFederated LearningCompanyComputational methods usedTechnology AbstractBioinformatics,Deep Learning,NLPArdigen is active in the field of laboratory information management systems,biological and clinical data analysis,Big Data integration,as well as custom application development.Machine Learning,Deep Learning(Convolutional neural networks),chemoinformaticsAtomNet is the first drug discovery algorithm to use a deep convolutional neural network.It has already explored questions in cancer,neurological diseases,antivirals,antiparasitics,and antibiotics.NLP,Deep Learning,Machine LearningDecodes open-and closed-access data on reagents such as antibodies and present published figures with actionable insights.Machine Learning,Deep Learning,symbolic AI,chemoinformaticsEvolved from text mining and semantic linking into knowledge graphs to tackle complex multifactorial diseases,identify novel targets,small molecule drug discovery and patient stratification.Machine Learning,Deep Learning,bioinformaticsAnalyze data from patient samples in both healthy and diseased states to generate novel biomarkers and therapeutic targets.Machine Learning,bioinformaticsAutomate selection,manipulation,and analysis of cells.Allows researchers to:Expedite development of cell lines and automate manufacturing of cellular therapeutics.Computational Methods Used by the Most Advanced AI Companies98Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractNLP,Deep Learning,Machine LearningProcess raw phenotypic,imaging,drug,and genomic data sets.Allows researchers to integrate rapid analytics and machine learning capabilities into existing business processes.NLP,Deep Learning,Machine LearningBioz has developed a search engine for Life Sciences community using natural language processing and machine learning technology to scan hundreds of millions of pages of complex and unstructured scientific papers on the web.Machine Learning,Deep Learning,chemoinformaticsBioxcel Corporation is a biopharmaceutical company pioneering the application of artificial intelligence and big data analytics integrated with drug development expertise.Machine Learning,Deep Learning,chemoinformatics,bioinformaticsC4X innovative DNA-based target identification platform(Taxonomy3(R)utilises human genetic datasets to identify novel patient-specific targets.Deep Learning,BioinformaticsIt is a deep learning company that uses innovative,computer-based methods to degrade undruggable targets and validate lead drug candidates in automated labMachine Learning,Deep Learning,symbolic AI,chemoinformatics,bioinformaticsCytoReasons access to unmatched proprietary and public data,combined with cutting-edge machine learning technologies,creates their unique biological models of disease,tissue and drug.Computational Methods Used by the Most Advanced AI Companies99Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,Deep Learning,NLPThe Data4Cure platforms modular architecture allows independent system components to handle integration and advanced analysis of heterogeneous data types spanning molecular,phenotypic and clinical data,both structured and unstructured.Machine Learning,Deep Learning,bioinformaticsDeep Genomics is using artificial intelligence to build a new universe of life-saving genetic therapies.Bioinformatics,Machine LearningDesktop Genetics is team of genome editing experts,bioinformaticians and data scientists,driven by the real-world impact of CRISPR technology.Their core technology,DESKGEN AI,was trained on the largest database of genome editing data in the world.Machine Learning,Deep Learning,high-performance computingEnvisagenics SpliceCore platform integrates proprietary machine learning algorithms,high performance computing,and RNA-splicing analytics to identify disease-specific alternatively spliced RNA that will function as therapeutic targets.Machine Learning,Deep Learning,bioinformaticsEuretos provides direct access to the cloud based discovery platform via user friendly application and also allows integration of company proprietary data and public data in a secure environment.Machine Learning,Deep Learning,bioinformatics,chemoinformaticsThe company uses ML for predicting ADME,novelty,synthetic accessibility,pharmacology of molecules.Computational Methods Used by the Most Advanced AI Companies100Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,Deep LearningBlending computational biology and AI-based methods,Genialis merges and models data at the intersection of clinical and translational medicine.Machine Learning,Deep LearningGNS Healthcare AI technology integrates and transforms a wide variety of patient data types into in silico patients which reveal the complex system of interactions underlying disease progression and drug response.Machine Learning,NLP,symbolic AI,chemoinformatics,bioinformaticsHealx AI platform uses natural language processing to extract disease knowledge from published sources and to complement biomedical databases and proprietary,curated data.Machine Learning,Deep Learning,cheminformaticsIktos has invented and is developing a technology based on DL for ligand-based de novo drug design,focusing on multi parametric optimization(MPO)Deep Learning,GANs,GANs Reinforcement Learning,symbolic AI,Machine Learning,chemoinformatics,bioinformaticsComprehensive DL pipeline.Biology:Signaling pathways,DNNs for target ID and HTS analysis.Chemistry:GANs-RL for novel molecule generation.NLP,Deep Learning,Machine LearningKyndi provides leading artificial intelligence software that can analyze long-form text and find actionable insights in a smarter,faster,and more explainable way.Computational Methods Used by the Most Advanced AI Companies101Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,chemoinformaticsWith a huge experience in Lead Generation,Lead Optimisation and method development the goal of the company is to accelerate the progress of our clients programmes.NLP,Deep LearningnferX uses state-of-the-art Neural Networks for real-time,automated extraction of knowledge from the commercial,scientific,and regulatory body of literature.Big data analytics;Deep Learning,Machine LearningDiscover connections between drugs and diseases at a systems level by analyzing of millions of raw human,biological,pharmacological,and clinical data points.Deep Learning,BioinformaticsPredict the therapeutic potential of food-derived bioactive peptides.Allows researchers to:cost-effectively develop highly targeted treatments for specific diseases from natural food sources.Machine Learning,Federated LearningOwkin combines the expertise in biology and machine learning to fuel precision medicine.Owkin facilitates access to real-world data by therapeutic area through its data connect service.Deep Learning(TensorFlow Keras base)Worlds first protein database specifically for Deep Learning and AI applications with full Keras and Tensorflow integration.Computational Methods Used by the Most Advanced AI Companies102Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractDeep Learning,Reinforcement LearningPhenomic predicts which cells will survive chemotherapy and identifies compounds that selectively target these resistant cells.It will then develop the compounds and bring them to market.Quantum Computing,Reinforcement Learning,ChemoinformaticsProteinQure is combining quantum computing,reinforcement learning,and atomistic simulations to design protein drugs.They can design peptide-based therapeutics and explore protein structures without crystal structures.Evolutionary algorithms,Machine LearningML-based structure based predictive models for potency and ADMET/PK properties of small molecules.Machine Learning,Deep LearningReviveMeds platform enables the rapid,high-throughput,and cost-effective application of metabolic data to discover new disease mechanisms for drug discovery and,simultaneously metabolomic biomarkers to identify which patients stand to benefit by targeting the disease mechanism.Machine Learning(stochastic gradient descent and branch-and-bound maximum likelihood optimization)The cryoSPARC System enables high-throughput structure discovery of proteins and molecular complexes from cryo-EM data with help of machine learning.Quantum physics;Machine LearningXtalPis ID4 platform provides accurate predictions on the physiochemical and pharmaceutical properties of small-molecule candidates for drug design,solid-form selection,and other critical aspects of drug development.Computational Methods Used by the Most Advanced AI Companies103Deep Pharma IntelligenceDEEP PHARMA INTELLIGENCE15 Notable R&D Use Cases of AI Application in BiopharmaPharmaChemical synthesisSmall drugs molecules 105Deep Pharma Intelligence2 Key AdvantagesAIMachine LearningDeep LearningCognitive Reasoning TechnologiesNatural Language ProcessingBiopharma utilizes living organisms(such as yeasts,bacterias,and mammalian cells)which are capable to produce complexly structured products such as proteins,hormones,RNA and DNA products,and virus capsids.Whereas Pharma relies on a classical chemical synthesis producing small drug molecules.However,both industries may benefit from AI-driven applications.To develop new small drug molecules or biologically-derived products,AI-driven data processing serves as a tool that allows minimising time consuming biological testings while helping to select the most promising products to test.BiopharmaLiving organisms(yeasts,bacterias,mammalian cells)Complex structures(proteins,DNA,RNA,hormones,viruses capsids)Introduction to Most Innovative R&D Approaches of AI in Biopharma AbbVie is a global,research-based biopharmaceutical company founded in 2013 following separation from Abbott.The companys mission is to use its expertise,dedicated people and innovative approaches to develop and market advanced therapies that address some of the complex and serious diseases.AbbVie does have a confidential project listed with Atomwise.Also,in September 2016,together with its partner AiCure,AbbVie announced how its AI-based patient monitoring p
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www.dka.globalArtificial Intelligence Industry in the UK Landscape Overview 2021:Companies,Investors,Influencers and Trends2Innovation EyeArtificial Intelligence Industry in the UK Landscape Overview 2021:Companies,Investors,Influencers and Trends(Second Edition),produced by Innovation Eye and powered by Big Innovation Centre and Deep Knowledge Analytics,presents an updated overview of the entire Artificial Intelligence(AI)Industry Ecosystem in the United Kingdom.It serves as a comprehensive follow-up to Innovation Eyes AI in UK Landscape Overview 2018(First Edition),produced in collaboration with the All-Party Parliamentary Group on Artificial Intelligence(APPG AI).This report(and its associated interactive mind maps linking AI companies to their investors)constitutes the most comprehensive survey of the UK AI Industry conducted to date,categorising and profiling almost 4,000 distinct AI-centric entities in the UK.It considers the funding of various UK AI projects and the geographical distribution of AI companies.It provides an overview of the most promising directions of AI development and application in the UK.For example,it identifies who invests in which AI companies and in which UK cities the AI research and entrepreneurship is located.Using text,graphics,figures,and analytics,it tells the story of the innovation landscape of AI in the UK and its main trends,including an overview of Government initiatives on AI in the period 2019-2021.It answers questions such as:Does the UK have the required resources to become a global hub for Artificial Intelligence?Has the UK marshalled enough resources across the industry,government,academia and thought leadership to build an innovation and investment ecosystem for AI development,applications,and cooperation?Does the UKs AI strategy have the resources,political will,and suitable regulation to cement a global leadership position for our AI Industry?IntroductionIntroduction2Methodology3Executive Summary4AI Industry in the UK Landscape Overview 20219Funding and Geographical Distribution of AI Companies by Sector11Key Influencers and Experts24The UKs AI Network and Talent Pool Enablers37The UKs National AI Strategy 2021:Overview 44Government Initiatives on AI:History and Main Features49Artificial Intelligence Roadmap58AI Policies and Ethics:COVID-19,Facial Recognition,and Education66AI in the UK:Prominent Reports on AI(2019-2021)72Conclusion80About Us 83Sources88Disclaimer89Appendix903Innovation EyeMethodology AI Industry in the UK Landscape Overview 2021Main ParametersThe present case study seeks to provide an extensive overview of the AI Industry in the UK.It achieves this by profiling all relevant entities and exploring the key trends and developments driving AI growth in the country.The report analyses 2,000 companies,1,500 investors,and 90 AI Hubs with AI programmes(including think tanks,tech-hubs,doctoral training centres,and events companies)which have,in turn,been categorised into 20 specific industry subsectors.The report also identifies more than 150 influencers and experts in the AI space(divided into specific areas of practical application,including Policy,Business,Academia,and Think Tanks).The selection of the AI-centric entities(see figure to the right)aims to deliver a comprehensive and up to date overview of the AI forefront in the UK across a wide variety of private and public sector domains.The entities have been selected by using public domain databases,open-source search engines,public and private sector reports,and media reports.The data and calculations on the main trends of the AI Industry in 2021 in the UK featured in this report have been aggregated from a wide variety of reputable and public data sources,including general and industry-specific databases,media and news reports,and conferences and government websites.While the information presented herein is believed to be reliable,the reports authors make no representation as to the accuracy or completeness of its constituent materials,information,and data.AI Companies2,000 Investors1,500 Influencers150 Prominent AI Reports20 AI Hubs and Think Tanks90 AI Categories20 Executive Swww.dka.global5Innovation EyeExecutive Summary Source:publications.parliament.uThe AI revolution is enormous.Increased use of Artificial Intelligence(AI)can bring major social and economic benefits to the UK,but there are also risks which need mitigation.Today,AI-based systems can already do things humans will simply never be able to accomplish.This has led to the AI Industry catching the focused attention of investors,entrepreneurs,researchers,and legislators at the government level.For example,to increase AI readiness and skills,the UK is creating new training and educational opportunities led by UK universities.Specific recommendations relating to staff training programs are part of these ongoing efforts.In a similar vein,investors invest in startups,a large proportion of which are based in London.Incidentally,65%of the UKs AI companies are headquartered in the UKs capital,making it the most attractive city for investment and talent.Total investment in the UKs AI companies is in excess of 13bn.However,the AI sector was not unaffected by COVID-19.In 2018-2019,investments in AI increased by 200%,while in 2020 they decreased by 64%,but recovered in 2021.The funding over these three periods stands at 9bn.If AI starts to make ethical and political decisions for us,this means that the study of ethics and ethics training is now more important than ever.We are currently witnessing transmission trends from ethical discussions on AI adoption into real-world usage,so governance in this area is vital.The UK has the potential to become the worlds leader in advanced AI systems,developing and using Machine Learning,computer vision,chatbots and AI assistants,robotic,Internet of Things,predictive analytics,search engines and language processing as well as intelligent data analytics.The UK and London,in particular is becoming a true innovation and investment epicentre for everything related to Artificial Intelligence.Our quantitative and qualitative analysis leads to this conclusion,finding combined efforts across all segments of business,investment,research,society and political will to:Ensure AI readiness through the UKs human capital,skills and diversity.Evident from our AI training and skills programmes.Accelerate R&D and Innovation and speed up its adoption to market,through investment in entrepreneurship and AI applications,in an ethical and sustainable way.Evident from our investment strategies.Build ecosystems and networks that attract foreign talents to enhance collaborations on AI between academia,industry,and government.Evident from our network organisations and talent pool enablers.Engage experts,stakeholders and citizen,participation through multiple,ongoing,open government consultations to build public trust in the decisions around the regulation and adoption of AI so we can realise the benefits and minimise the risks.Lead AI governance and regulation though stimulating ethical business models on AI-driven implementations and use.This underpins how we think and who we are.Invest in the UKs digital infrastructure,including data and cyber security.Here we are operating in catch-up.Our analysis underpinning these results finds three facts that encapsulate this.They are described on the next three pages,but the reader must consult the entire report for full evidence,analysis,illustrations,and graphics.The UK is the host of a highly sophisticated AI innovation ecosystem bringing together investment confidence,talent,industry growth,and an AI-community spirit.The UK,and especially London,has unique potential to become a true epicentre of purposeful,innovative,and safe international Blockchain integration and cooperation.The 2021 analysis and report identify and profile more than 2,000 AI-centric companies across 20 AI sectors and 50 cities in the UK.In particular,the 2021 UK AI landscape overview breaks down,on a company-by-company basis,more than 13 billion worth of investments from 1,500 investors into these AI companies.Additionally,it profiles hundreds of UK AI experts across more than 90 AI-centric hubs including think tanks,tech-hubs,doctoral training centres,and AI events.Finally,it provides a rundown of AI-friendly government initiatives and policy bodies that create AI initiatives,which include creating and delivering the National AI Strategy.The majority of investments in the UK AI space have been in FinTech(25.84%),Marketing and Advertising(22.85%),Healthcare(12.54%),and Entertainment(6.46%).There is also significant investment in Security(5.88%)and Data Analytics companies(5.24%).Investment in GovTech(0.86%),Energy(0.29%),and Transportation(1.85%)is much lower.This relatively small investment in AI GovTech and the likes indicates how there is still underinvestment in public-purpose sectors compared to their huge potential.But we can still identify several government-related bodies involved in AI initiatives such as the Office for AI and the AI Council,Innovate UK,the UK Home Office,the Intellectual Property Office,the Information Commissioners Office,Government Digital Services,NHSX,and more.We see how the UK and,in particular,London is becoming a true innovation and investment epicentre for everything AI-related.Almost 1,300(or 65%)of the UKs 2,000 AI companies are headquartered in London.One could conclude that the entrepreneurial AI community is located close to finance,marketing,and regulatory bodies that are needed to grow the AI community.London,as Europes marketing and advertisement centre and financial capital,is now also innovating a new ecosystem in AI investment,development,deployment,and adoption.6Innovation Eye1/3:INVESTMENT CONFIDENCE IN ARTIFICIAL INTELLIGENCE ENTREPRENEURSHIP IS HIGH AND GROWING Executive Summary 7Innovation EyeThe UK hosts a highly sophisticated AI innovation ecosystem integrating the ingredients for a vibrant and dynamic AI Industry:science,technology,talent,business models,and entrepreneurship with financial backing.Thus,the UKs Artificial Intelligence innovation and investment ecosystem brings together:An entrepreneurial AI Industry system which is becoming a magnet for entrepreneurial finance.A top-talent and AI science system from our education system,research base,and universities.Industry,finance,and talent systems are mixed with a network of AI think tanks and events companies that are building the UKs world-leading AI communities.Thus,in the UK we are closing the gap between the research base,AI business models,and market-adoption.We are also closing the gap between a growing AI Industry and entrepreneurial finance.The highly networked AI talent pool comes from a network of AI initiatives including:1.Both research and teaching programmes at UK universities(in excess of 16 doctoral training programmes,most of which are receiving grants from UK Research and Innovation UKRI including the Engineering and Physical Sciences Research Council EPSRC)2.AI networking and events companies(such as CogX,Wearable Technology Show,and Big Data and AI World.),3.Think-tanks(including the Ada Lovelace Institute,Big Brother Watch,Institute for the Future of Work,Teens in AI,and Big Innovation Centre)4.The All-Party Parliamentary Group on AI(APPG AI)functions as the permanent authoritative voice within the UK Parliament(the House of Commons and House of Lords)on all AI-related matters,while engaging with the entire network to bring experts and use cases to inform parliamentarians.It is accompanied by a video and report series,plus a community platform(online and onsite).A nation of AI expertsThe UK is the host of hundreds of AI experts and influencers across businesses,academia,think tanks,and policy.2/3:A HIGHLY INTEGRATED ARTIFICIAL INTELLIGENCE INNOVATION ECOSYSTEM OF TALENTExecutive Summary 8Innovation EyeExecutive Summary The UK governments AI strategy must catalyse some of these opportunities.The UK has recognised AI as a huge opportunity and indeed the UK governments Industrial Strategy white paper identified AI and Data as one of four Grand Challenges(together with Future of Mobility,Clean Growth,and Ageing Society).This was followed up by a range of government initiatives,leading to the AI strategy published towards the end of 2021.There is strong political will for AI technology and adoption,but the UKs AI markets and industry are moving fast.The UK is a leader in Ethical AI.The fast growth in AI development and adoption needs to be accompanied by increased regulatory engagement in this space,stimulating institutional bodies and engagement to co-create AI strategies,as well as rules,norms,and standards in the AI implementation space.Examples of government-related bodies that make AI initiatives include the Office for AI co-producing the AI Strategy,through the AI council publication consultation as a follow up to the AI Roadmap.Other initiatives include the Online Harms White paper from the Department for Digital,Culture,Media and Sport and the Home Office,also produced through public consultation.Other AI-related strategies and papers co-produced though open consultations since 2019 include those on the UK National Data Strategy(NDS)by the Department for Digital,Culture,Media and Sport with the aim of enabling the UK to build a world-leading data economy while ensuring public trust in data use.The open consultation for Artificial Intelligence and Intellectual Property:copyright and patents is a current initiative by the Intellectual Property Office.The House of Lords Liaison Committee produced its own consultation of AI in the UK,published with the sub-title No Room for Complacency.There is also commissioned research to inform on The Automated Facial Recognition Guide to Ethical and Legal Use,designed by the British Security Industry Association to improve peoples safety and wellbeing.The Centre for Data Ethics and Innovations review into bias in algorithmic decision making is a similar example.3/3:THE UKs ARTIFICIAL INTELLIGENCE ENTREPRENEURSHIP,TALENT,AND INVESTMENT IS IN CLOSE PROXIMITY WITH REGULATORSAgricultureTransportationSupply ChainHRSecurityReal EstateOthersLegalTechInsurTechIndustrial EngineeringGovTechHealthcareFinTechEntertainmentEnergyEducationDevelopersConsulting&OutsourcingData AnalyticsMarketing&AdvertisingCompanies-2000 Investors-1500 Hubs-90 AI Industry in the UKLandscape Overview 2021CompaniesInvestorsHubsCentre for Doctoral TrainingInternational HubsAI Events CompaniesGovernment Related AgenciesHubs10Innovation EyeFunding and Geographical Distribution of AI Companies by Swww.dka.global12Innovation EyeComparison of AI in the UK First and Second EditionsArtificial Intelligence Industry in the UK 2018 Artificial Intelligence Industry in the UK 20213.1bn 93%Annual funding of the UKs AI Industry1,500 154%Total Number of Investors in AI Industry 2,000 more than 1000New AI Companies1.6bn6001,00013Innovation EyeKey Findingsof UK AI start-ups and scale-up companies are headquartered in London65%is the total growth in the AI funding to the UKs AI Industry over past 3 years(2019-2021)is the total growth in the number of investors in the AI Industry over the past 3 years(2019-2021)Total AI funding of the UKs AI Industry to date13.8bn Total number of investors in UKs AI Industry to date1500 AI sectors by number of AI companies are Marketing&Advertising,FinTech,and Consulting(ca.47%of total AI companies)The number of UK AI companies that received ca.26%of total AI funding in the past 3 years(2019-2021)15AI sectors by of all time Total AI Funding are FinTech,Marketing&Advertising,and Health(ca.60%of total AI funding)is the total growth in the number of AI companies in the past 3 years(2019-2021)Total AI companies in the UKs AI Industry2000 9bn 900 1000Top 3Top 3*Funding includes investments,donations,grants and subsidies.14Innovation EyeBreakdown of AI Companies in the UK by SectorMarketing&AdvertisingFinTechConsulting&OutsourcingData AnalyticsDevelopersEntertainmentEducationSecurityHuman Resources(HR)HealthcareInsurTechGovTechIndustrial EngineeringLegalTechTransportationReal EstateAgricultureEnergySupply Chain ManagementOthersIn the UK,there are more than 2,000 companies that have AI in their strategic toolset.More of these belong to the Marketing&Advertising sector(28.04%)than any other sector.In the Top 5 sectors,Marketing&Advertising is followed by FinTech,Consulting&Outsourcing,Data Analytics,and Developers.28.04.08%8.23%7.53%4.69%3.94%3.74%3.54%3.54%3.14%2.54%2.00%1.60%1.30%1.30%0.95%0.95%0.90%0.45Innovation EyeAI in the UK:Geographic DistributionAfter London,Cambridge is the second enterprise hub for AI in the UK.Other locations with some of the highest concentrations of AI enterprises in the UK include Edinburgh,Manchester,Oxford,and Bristol.Cities with the Highest Concentration of AI Companies(except London)More than 65%of the UKs AI companies are headquartered in London2.40%1.63%1.48%1.38%1.17%0.87%0.76%1.63%0.82%0.76%London is the world-leading hub of the UK Artificial Intelligence industry.Almost 1,300 high-growth AI companies are located in the capital.In that regard,London is a centre for growing talent as well as an attractor of it.16Innovation EyeTotal Funding*Amount for Companies by SectorFinTechMarketing&AdvertisingHealthcareEntertainmentSecurityData AnalyticsHuman Resources(HR)EducationConsulting&OutsourcingOthersInsurTechLegalTechTransportationDevelopersGovTechIndustrial EngineeringEnergySupply Chain ManagementAgricultureReal Estate3.57bn(25.84%)3.15bn(22.84%)1.73bn(12.54%)892M(6.46%)812M(5.88%)724M(5.24%)515M(3.73%)381M(2.76%)376M(2.72%)327M(2.37%)266M(1.93%)222M(1.61%)207M(1.50%)189M(1.37%)112M(0.81%)105M(0.76%)87M(0.63%)64M(0.46%)51M(0.37%)25M(0.18%)Funding is concentrated in the FinTech and Marketing&Advertising sectors.13.8bn Total Funding*Funding includes investments,donations,grants and subsidies.17Innovation EyeFunding*Amount for Companies by Sectors for the last 3 years*Funding includes investments,donations,grants and subsidies.201920202021FinTechHealthcareMarketing&AdvertisingData AnalyticsSecurityConsulting&OutsourcingEducationEntertainmentHuman Resources(HR)OthersInsurTechTransportationDevelopersGovTechLegalTechIndustrial EngineeringAgricultureSupply Chain ManagementEnergyReal Estate2.77bn(30.64%)1.77bn(19.59%)1.32bn(14.61%)519M(5.73%)447M(4.94%)359M(3.97%)324M(3.58%)252M(2.79%)248M(2.73%)206M(2.27%)191M(2.11%)149M(1.64%)142M(1.57%)115M(1.27%)76M(0.84%)47M(0.52%)38M(0.42%)25M(0.27%)24M(0.27%)13M(0.15%)The FinTech sector is the clear leader in funding amount for AI companies,collecting more than 2.7bn in the 2019-2021 period.Funding levels in Healthcare sector has remained strong over the past year,notably bucking a general downward trend for AI Industry funding during the COVID-19 pandemic.9bn Total Funding for the last 3 years18Innovation EyeQuarterly Change in the Flow of Funding,2018-2021*Funding includes investments,donations,grants and subsidies.4bn3bn2bn1bn0The flow of funding shows fluctuating seasonal dynamics during the 2018-2021 period.The third quarter is traditionally the hottest investment season.Overall,the biggest year for funding was 2019,during which the AI sector raised over 3bn.3.1bn2.6bn3.3bn1.6bnQ1Q2Q3Q419Innovation EyeQuarterly Change in the Flow of Funding,2018-2021*Funding includes investments,donations,grants and subsidies.Q12.0bn1.5bn1.0bn500M0Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q42018201920202021476M421M1.5bn415M702M532M507M861M344M1.1bn720M945M618M20Innovation EyeDynamic of Funding by Top 5 AI Sectors*Funding includes investments,donations,grants and subsidies.Q1 2014Q1 2015Q1 2016Q1 2017Q1 2018Q1 2019Q1 2020Q1 2021Q1 20221.1bn1.0bn900M800M700M600M500M400M300M200M100M0MEntertainmentMarketing&AdvertisingHealthcareFinTechSecurity21Innovation EyeTop 15 UK AI Companies by Total Funding*Funding includes investments,donations,grants and subsidies.OakNorthThe UKs AI market has been blossoming over the last three years;businesses continue to develop,while new startups have also continued to emerge.The Top 15 UK AI Companies account for 26.21%of total UK AI funding.OakNorth,the cloud software provider,stands out as the top funded company,receiving more than 791M.GraphcoreBabylonExscientiaMoloBenevolentAIIHS MarkitDarktraceBlue PrintOnfidoPrivitarBlipparWejoMoneyfarmBehavox5.6%3.7%3.4%2%1.9%1.6%1.3%1.2%0.98%0.97%0.8%0.73%0.7%0.68%0.65Innovation EyeTop 30 UK AI Companies by Funding*Amount*Funding includes investments,donations,grants and subsidies.OakNorth1GraphcoreBabylon Health Exscientia4Molo5BenevolentAI6Darktrace8Blue Prism9Onfido10IHS Markit723Privitar11BlipparWejoMoneyfarm14Behavox15ComplyAdvantage16Thought Machine18Digital Surgery19Touch Surgery20Featurespace171213XMOS21CeraPartnerizeOxbotica24Quantexa25FiveAI26Infinity SDC28FintechOS29Qubit30CloudFactory272223791M518M479M 283M267M222M190M175M139M138M114M104M99M97M92M82M81M80M74M74M72M69M68M68M67M60M59M58M57M57MWayraSeedcampPlug and PlayBarclays AcceleratorDowning VenturesEntrepreneur FirstCrowdcubeAI Seed23Innovation EyeTop 30 UK Investors in AI Companies1AlbionVCAmadeus Capital PartnersAnthemis Group4Ascension Ventures5Atomico68910723Episode 111Founders FactoryFuture FiftyGuinness Asset Management14Innovate UK15L Marks16LocalGlobe18London Co-Investment Fund19Notion Capital20Level3917121321SFC CapitalStartupbootcamp24Tech Nation Fintech25Techstars London Accelerator26Upscale28Venrex2930The Future Fund272223Key Influencers and Ewww.dka.global25Innovation EyeKey Influencers and Experts 2020/2021Academia(1/2)Sir Anthony SeldonUniversity of BuckinghamDr.Carl Benedikt FreyUniversity of OxfordDame Wendy HallUniversity of SouthamptonProf.Chris ReedUniversity of DundeeDr.Daniel SusskindBalliol College,University of OxfordProf.David BarberUCL Centre for Artificial IntelligenceProf.Edgar WhitleyLondon School of EconomicsProf.Huw PriceCentre for the Study of Existential Risk,University of CambridgeProf.Jonathan HaskelImperial CollegeProf.Luciano FloridiUniversity of Oxford-Oxford Internet InstituteProf.Kaska Porayska-PomstaUCL Institute of Education(IOE)Prof.Maggie BodenUniversity of SussexDr.Aidan OSullivanUCL and UNESCOProf.Ivan TyukinVisual Intelligence Lab,University of LeicesterLindsey ChiswickMetropolitan Police ServiceProf.Martin InnesCrime and Security Research Institute,Cardiff University26Innovation EyeKey Influencers and Experts 2020/2021Academia(2/2)Prof.Nadia BerthouzeUCL Interaction CentreProf.Nick BostromFuture of Humanity Institute,University of OxfordSir Nigel ShadboltUniversity of OxfordProf.Nigel CrookInstitute for Ethical AI at Oxford Brookes UniversityProf.Phoebe MooreUniversity of LeicesterProf.Rose LuckinUCL Knowledge LabProf.Ryan AbbottUniversity of SurreyProf.Sandra WachterUniversity of Oxford-Oxford Internet InstituteProf.Seeta Pea GangadharanLondon School of EconomicsDr.Stephen CaveLeverhulme Centre for the Future of IntelligenceProf.Shannon VallorEthics of Data and Artificial Intelligence at the Edinburgh Futures Institute(EFI)Prof.Tim SpectorKings College LondonDr.Zeynep EnginUCLProf.Michael ThomasBirkbeck,University of LondonProf.Mike WooldridgeUniversity of OxfordPerdita FraserThe University of Edinburgh 27Innovation EyeKey Influencers and Experts 2020/2021Business(1/3)Ali ParsaBabylon HealthAndy PardoeWisdom Works GroupAzeem AzharExponential ViewCharles KerriganCMS Tax LawCharles RadclyffeEthicsGradeDr.Chris FrancisSAPDr.Christine ChowHSBCCori CriderFoxgloveDel AlibocusDRE DIGITAL LIMITEDDemis HassabisDeepMind TechnologiesAdrian Joseph OBEBT GroupAmy ChallenShellCaroline GorskiRolls RoyceDavid ShortBAE Systems28Innovation EyeKey Influencers and Experts 2020/2021Business(2/3)Joseph GeorgeDufrainDr.Keith GrimesBabylon HealthLucy HolmesOmni TelemetryDr.Laura DouglasMy LevelsMalika MalikMicrosoftMaria AxentePwCJohn BuyersOsborne ClarkeJacob TurnerFountain Court ChambersBaroness Joanna Shields OBEBenevolentAIJustin AndersonAnderson StrategyGerman BencciCode Your FutureFilomena La PortaEnzenEuan CameronPwCEileen Burbidge MBEPassion Capital29Innovation EyeKey Influencers and Experts 2020/2021Business(3/3)Dr.Priya Lakhani OBECENTURY TechRaja SharifFarma TrustRob McCargowPwCRichard ChiumentoRialtoDr.Scott SteedmanBritish Standards InstitutionSulabh SoralDeloitteTamara QuinnOsborne ClarkeDr.Tirath VirdeeCapitaPeter ScottAuthorDr.Peter WaggettIBM Research EuropeMatt CeluszakElement HumanMurray MorrisonTassomai30Innovation EyeKey Influencers and Experts 2020/2021Think Tanks(1/2)Sir Adrian Smith FRSThe Alan Turing InstituteDr.Adrian WellerThe Alan Turing InstituteAnna ThomasInstitute for the Future of WorkAndrew PakesProspectDr.Bertie MllerThe Society for the Study of AI and Simulation of Behaviour(AISB)Prof.Birgitte AndersenBig Innovation CentreCarly KindAda Lovelace InstituteCharlie MuirheadCognitionXDame Ottoline LeyserUK Research and Innovation(UKRI)David PetrieICAEWElena SinelTeens in AIDr.Desiree RemmertBig Innovation CentreDr.Florian OstmannThe Alan Turing InstituteHayaatun Sillem CBERoyal Academy of EngineeringIvana BartolettiWomen Leading in AI NetworkDr.Abigail GilbertInstitute for the Future of WorkJacqueline de Rojas CBETechUKJames FarrarWorker Info Exchange31Innovation EyeKey Influencers and Experts 2020/2021Think Tanks(2/2)James KingstonDataswiftDr.Jeremy SilverDigital CatapultDr.Jeni Tennison OBEOpen Data InstituteJohn TasioulasThe Institute for Ethics in AISir Mark WalportImperial College AHSCMike ReddingtonBritish Security Industry AssociationNaomi Climer CBEInstitute for the Future of WorkDr.Natalie BannerWellcome TrustOlly BustonFuture AdvocacyShirin BahainHarris FederationRoger TaylorAccentureSilkie CarloBig Brother WatchTabitha GoldstaubCognitionXUday NagarajuAI Policy LabsMary TowersTrades Union Congress(TUC)Pauline NorstromBritish Security Industry Association(BSIA)Simon McDougallInformation Commissioners OfficeSue DaleytechUK32Innovation EyeKey Influencers and Experts 2020/2021All-Party Parliamentary Group on AI(APPG AI since 2019)Actively engaged Layla Moran MP(Liberal Democrat)MemberBaroness Kate Rock(Conservative)Vice-ChairBaroness Susan Kramer(Liberal Democrat)Vice-ChairBaroness McGregor-Smith(Conservative)Vice-ChairCarol Monaghan(SNP)Vice-ChairChris Green(Labour)Vice-ChairChristopher Holmes(Conservative)Vice-ChairDarren Jones(Labour)Vice-ChairJustin Madders(Labour)Vice-ChairLord Broers(Crossbench)Vice-ChairLord Clement-Jones(Liberal Democrat)Co-ChairLord Janvrin(Crossbench)Vice-ChairLord Haskel(Labour)MemberLord Willetts(Conservative)Vice-ChairMark Hendrick MP(Labour)Vice-ChairStephen Metcalfe(Conservative)APPG AI Co-ChairThe Earl of Erroll(Crossbench)MemberThe Rt Revd Dr Steven Croft(Bishop)Vice-Chair33Innovation EyeKey Influencers and Experts 2020/2021House of Lords Liaison Committee on AI 2020(Chair and Members)Baroness Hayter of Kentish Town(Labour)MemberBaroness Joan Walmsley(Liberal Democrat)MemberLord Bradley(Labour)MemberFrederick Curzon(Conservative)MemberLord Davies of Oldham(Labour)MemberLord Judge(Crossbench)MemberLord Low of Dalston(Crossbench)MemberLord Lang of Monkton(Conservative)MemberLord McFall of AlcluithParliamentChairLord Tyler(Liberal Democrat)MemberLord Smith of Hindhead(Conservative)Member34Innovation EyeJulia Lopez MPDepartment for Digital,Culture,Media and SportKey Influencers and Experts 2020/2021Amanda Solloway MPDepartment for Business,Energy and Industrial Strategy Chi Onwurah MPLabour PartyChris Philp MPDepartment for Digital,Culture,Media and SportElizabeth Denham CBEInformation Commissioners OfficeEllis ParryInformation Commissioners OfficeDr.Indra JoshiNHSXDr.James HadlowNHSJoanna DavinsonUK Home OfficeLorna GrattonUK Government InvestmentsPaul Willmott MPUK Government Central Digital and Data OfficeMatthew HancockDepartment of Health and Social CareSana KhareganiGovernment Office for AISanu de LimaDepartment for Business,Energy and Industrial StrategyThe Rt Hon Kwasi Kwarteng MPDepartment for Business,Energy and Industrial StrategyNadine Dorries MPDepartment for Digital,Culture,Media and SportTom ReadGovernment Digital ServiceGovernment and Shadow CabinetAI Engaged Civil Servants Tanmanjeet Singh Tan Dhesi MPLabour Party35Innovation EyeSource:TheyWorkForYouAll Mentions of AI in Parliamentary Speeches:House of Commons6 occurrencesHouse of Commons:8 to 45 occurrences Chi Onwurah MP(Labour)Tan Dhesi MP(Labour)Matthew Hancock MP(Conservative)Margot James MP(Independent)Greg Clark MP(Conservative)(Commons)Nadine Dorries(Conservative)Alan Mak MP(Conservative)Chris Skidmore MP(Conservative)Claire Perry MP(Conservative)Amanda Solloway MP(Conservative)Darren Jones MP(Labour)Sam Gyimah MP(Independent)Jim Shannon MP(DUP)Matt Warman MP(Conservative)Chris Stephens MP(Scottish National Party)Kirsten Oswald MP(Scottish National Party)Richard Harrington MP(Independent)Justin Madders MP(Labour)Jo Swinson MP(Liberal Democrat)Sajid Javid MP(Conservative)Andrew Griffith MP(Conservative)Aneurin Bevan MP David Davis MP(Conservative)Jackie Doyle-Price MP(Conservative)Caroline Dinenage MP(Conservative)Susan Kramer MP(Conservative)Sadiq Khan MP(Labour)Daniel Zeichner MP(Labour)Lee Rowley MP(Conservative)Theresa May MP(Conservative)Stephen Metcalfe MP(Conservative)Victoria Atkins MP(Conservative)5 occurrences Oliver Dowden MP(Conservative)Jim Knight MP(Labour)Bob Seely MP(Condervative)Paul Flynn MP(Labour)David Prior MP(Condervative)Kemi Badenoch MP(Conservative)Robert Buckland MP(Conservative)Henry Smith MP(Condervative)Nicola Blackwood MP(Conservative)Graham Stuart MP(Conservative)Oliver Letwin MP(Independent)Eddie Hughes MP(Conservative)Chris Green MP(Conservative)Des Browne MP(Labour)Boris Johnson MP(Conservative)7 occurrences Gillian Keegan MP(Conservative)Elizabeth Truss MP(Conservative)George Freeman MP(Conservative)Tam Dalyell MP(Labour)All Mentions of AI in Parliamentary Speeches:House of Commons36Innovation Eye4 OccurrencesSource:TheyWorkForYouLord Clement-Jones(Liberal Democrat)Lord Haskel(Labour)Lord Ashton of Hyde(Conservative)Lord Holmes of Richmond(Conservative)Lord Taylor of Warwick(Non-affiliated)Lord Henley(Conservative)Lord Baker of Dorking(Conservative)Baroness Rock(Conservative)Lord Bethell(Conservative)Baroness Barran(Conservative)Lord Hodgson of Astley Abbotts(Conservative)Lord OShaughnessy(Conservative)Lord McNally(Liberal Democrat)Lord Stevenson of Balmacara(Labour)Lord St.John of Bletso(Crossbench)Lord Browne of Ladyton(Labour)Lord Howell of Guildford(Conservative)The Bishop of Oxford(Bishop)Baroness Grender(Liberal Democrat)Baroness Goldie(Conservative)Baroness Kramer(Liberal Democrat)Baroness Finlay of Llandaff(Crossbench)Baroness Blackwood of North Oxford(Conservative)Viscount Ridley(Conservative)Lord Knight of Weymouth(Labour)Baroness Hamwee(Liberal Democrat)Lord Prior of Brampton(Non-affiliated)Lord Callanan(Conservative)Lord Rees of Ludlow(Crossbench)Lord Mann(Non-affiliated)Lord Johnson of Marylebone(Conservative)Lord Patten(Conservative)Lord Mair(Crossbench)Lord Campbell of Pittenweem(Liberal Democrat)Lord Alton of Liverpool(Crossbench)Baroness Thornton(Labour)Lord Paddick(Liberal Democrat)Baroness Warnock(Crossbench)Lord Tyler(Liberal Democrat)Lord Warson of Invergowrie(Labour)Lord West of Spithead(Labour)Lord Patel(Crossbench)Lord Freyberg(Crossbench)Lord Mitchell(Labour)Lord Blunkett(Labour)Lord Davies of Stamford(Labour)Lord Agnew of Oulton(Conservative)Lord Houghton of Richmond(Crossbench)Baroness Blackwell(Labour)Lord Austin of Dudley(Non-affiliated)Lord Wallace of Saltaire(Liberal Democrat)Lord Hunt of Kings Heath(Labour)Viscount Younger of Leckie(Conservative)Baroness Bennett of Manor Castle(Green)Lord Coaker(Labour)Lord Tunnicliffe(Labour)Lord Young of Cookham(Conservative)Lord Scriven(Liberal Democrat)Lord Kakkar(Crossbench)3 OccurrencesHouse of Lords:5 to 26 occurrences All Mentions of AI Parliamentary Speeches:House of LordsThe UKs AI Network and Talent Pool Ewww.dka.global38Innovation Eye40Think Tanks and Research HubsUKs AI Network and Talent Pool Enablers 24Doctoral Training Centres17Government Related Initiatives90 Total Hubs in UKs AI Network and Talent Pool Enablers4Events Companies Hosting Global Conferences8International Bodies Hosting AI Initiatives39Innovation EyeUKs AI Network and Talent Pool Enablers Think Tanks and Research HubsAda Lovelace InstituteAI Policy LabsArtificial Intelligence Applications Institute(AIAI)Big Brother Watchdog Big Innovation CentreBristol Intelligent Systems LabBritish Security Industry Association(BSIA)Centre for Intelligent Systems and Their ApplicationsCentre for the Study of Existential RiskEdinburgh Centre for RoboticsFuture AdvocacyFuture AI and Robotics for Space Hub(FAIR-SPACE)Future of Humanity Institute OxfordInstitute for Adaptive and Neural ComputationInstitute for the Future of WorkInstitute of Perception,Action and BehaviourBritish Interactive Media Association Deep Knowledge AnalyticsDemosEarlham Institute40Innovation EyeUKs AI Network and Talent Pool Enablers Think Tanks and Research HubsLeverhulme Centre for the Future of IntelligenceNational Centre for Nuclear RoboticsOxford Internet InstituteOxford Robotics InstituteRobotics and Artificial Intelligence for Nuclear(RAIN)HubRoyal Academy of EngineeringThe Royal SocietySociety for the Study of Artificial Intelligence and Simulation of Behaviour Strategic AI Research CentreTech UKTeens in AI The Alan Turing InstituteUCL Centre for Artificial intelligenceWomen in Tech in LondonThe Institute for Ethics in AIUKRI Centre for Doctoral Training in Interactive Artificial IntelligenceNestaNuffield FoundationTech Nation41Innovation EyeDoctoral Training CentresUKRI Centre for Doctoral Training in AI and MusicUKRI Centre for Doctoral Training in AI for the Study of Environmental Risks(AI4ER)EPSRC Centre for Doctoral Training in Computational Statistics and Data Science(COMPASS)AI-enabled Healthcare SystemsEPSRC Centre for Doctoral Training in CybersecurityEPSRC Centre for Doctoral Training in Future Propulsion and Power(TURBO)UKRI Centre for Doctoral Training in Safe and Trusted AIFoundational Artificial IntelligenceUKRI Centre for Doctoral Training in Interactive AIUKRI Centre for Doctoral Training in Speech and Language Technologies EPSRC Centre for Doctoral Training in the Advanced Characterisation of MaterialsEPSRC Centre for Doctoral Training in Geometry and Number Theory at the InterfaceUKRI Centre for Doctoral Training in Environmental IntelligenceUKRI Centre for Doctoral Training in Accountable,Responsible and Transparent AIMathematics for Real-World Systems II Centre for Doctoral TrainingEPSRC Centre for Doctoral Training in Soft Matter for Formulation and Industrial InnovationUKRI Centre for Doctoral Training in AI,Machine Learning and Advanced ComputingUKRI Centre for Doctoral Training in AI for Medical Diagnosis and CareEPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment UKRI Centre for Doctoral Training in AI for Healthcare UKRI Centre for Doctoral Training in Natural Language ProcessingEPSRC Centre for Doctoral Training in Distributed AlgorithmsEPSRC Centre for Doctoral Training in Mathematical Modelling,Analysis and Computation EPSRC Centre for Doctoral Training in Modern Statistics and Machine LearningUKs AI Network and Talent Pool Enablers 42Innovation EyeGovernment/Policy Bodies That Produce AI InitiativesAll-Party Parliamentary Group on AI Centre for Data Ethics and InnovationCompetition and Markets Authority Digital CatapultGovernment Office for AIHouse of Lords Liaison Committee on AIIndustrial Strategy Challenge Fund:AI and Data EconomyInformation Commissioners OfficeInnovate UKSurveillance Camera CommissionerOpen Data InstituteThe AI CouncilThe British AcademyThe Committee on Standards in Public Life The Artificial Intelligence in Health and Care AwardUK Research and Innovation(UKRI)The Biometrics and Forensics Ethics Group UKs AI Network and Talent Pool Enablers 43Innovation EyeUKs AI Network and Talent Pool Enablers Council of EU and AI G20Global Partnership on AI(GPAI)OECD InnovationOECD Network of Experts Classification of AIUNESCOWomen in AI(WAI)World Economic ForumCogXDigital Health World CongressLondon Tech WeekWearable Technology ShowAI World Congress 2021The AI SummitBig Data&AI WorldInternational Bodies Hosting AI Initiatives With Links to the UK Policy SectorOther ConferencesUK AI Events Companies Hosting Global Conferences(at least 1 per year or more)The UKs National AI Strategy 2021:Overview www.dka.global45Innovation EyeSources:gov.ukGeneral OutlinesThe National AI Strategy builds on the UKs current strengths and represents the start of a step-change for AI in the UK,recognising that maximising the potential of AI will increase resilience,productivity,growth and innovation across the private and public sectors.Building on the strengths in AI will take a whole-of-society effort that will span the next decade.This is a top-level economic,security,health,and wellbeing priority.Future ImpactBenefits of AI adoption shared across every region and sectorUK maintains its positionas a global leader in AI R&DGrowth in the UKs AI sector,contributing to UK GDP growthProtecting and promoting fundamental British valuesStrong domestic AI capabilities to address National Security issuesOutcomesInvesting in the long-term needs of the AI ecosystemEnsuring AI benefits all sectors and regionsGoverning AI effectivelyA growing UK supplier baseReduced competition for AI skills New AI scientific breakthroughsGreater workspace diversity Increased diversity in applied AI Applied AI technologies to new use cases Increased investment in UK AI companies Greater public value for moneyWider AI adoption in industries and regions Public sector as exemplar for AI procurement and ethicsGreater UK AI exportsCertainty for the UK AI ecosystemImproved public trust in AI UK maintains its position as a global leader in AIIncreased responsible innovation46Innovation EyeGovernment InvestmentsSources:gov.ukNotes:*This portfolio of investment includes,but is not limited to Over 2.3bnhas been invested in Artificial Intelligence by the government across a range of initiatives since 2014.has been invested in Connected and Autonomous Mobility(CAM)technology to develop the future of mobility in the UK.250MHave been offered in a new industry-funded AI Masters programme for AI and data science conversion courses.This includes up to 1,000 funded scholarships.Up to 2,500 placeshas been invested in the Alan Turing Institute to support the Turing AI Fellowships to develop the next generation of top AI talent.Over 46Mhas been granted to develop the NHS AI Lab at NHSX to accelerate the safe adoption of Artificial Intelligence in health and care.250Mhas been pledged in support 16 new AI Centres for Doctoral Training at universities across the country and delivering 1,000 new PhDs over 5 years.Up to 100Mhas been invested in UK AI companies through the British Business Bank for the growing AI sector.Over 372Mhas been invested in the Hartree National Centre for Digital Innovation through the UKRI.172M47Innovation EyeChallenges and OpportunitiesCompliance with infrastructural requirements is a challenge as they are often greater for AI services than in Cloud/Software as Service systems.Also,some models require access to expensive,high-performance computing.Infrastructural RequirementsA systems autonomy raises unique questions around liability and fairness as well as questions of transparency and bias which arise from decisions made by AI systems.RegulationsEnsuring people from all backgrounds and parts of the UK participate and thrive in this new AI economy.MoralityRepresentative data that is not perpetuating new forms of bias in society.Peoples participation from diverse backgrounds in the development and deployment of AI systems is essential.SocietyMultiple skills are required to develop,validate,and deploy AI systems,and the commercialisation and product journey can be longer and more expensive because so much starts with fundamental R&D.Skills and CommercialisationIncreasing the UKs human capital from a diverse labour supply;creating a wider range of AI services;etc.EconomySources:gov.uk48Innovation EyeGovernment Interconnected ProgrammesThe Plan for Growth and the recent Innovation Strategy both recognise the need to develop a diverse and inclusive pipeline of AI professionals with the capacity to supercharge innovation.The Integrated Review will help to discover new paths for UK excellence in AI to deliver prosperity and security at home and abroad and shape the open international order of the future.The National Data Strategy sets out the UK Governments vision to harness the power of responsible data use to boost productivity,create new businesses and jobs,improve public services,support a fairer society,and drive scientific discovery,positioning the UK as the forerunner of the next wave of innovation.The Plan for Digital Regulation sets out the UK governments pro-innovation approach to regulating digital technologies in a way that drives prosperity and builds trust in their use.The upcoming National Cyber Strategy will continue the drive to secure emerging technologies,including integrating security into the development of AI.A new Defence AI centre will form a key piece of the modernisation of Defence.The upcoming National Resilience Strategy will,in part,focus on how the UK will stay on top of technological threats.The National Security Technology Innovation exchange(NSTIx)is a data science and AI co-creation space that brings together National Security stakeholders,industry and academic partners to build better national security capabilities.The Digital Strategy will build on Department for Digital,Culture,Media and Sports(DCMS)Ten Tech Priorities to further set out the Governments ambitions in the digital sector.Sources:gov.ukGovernment Initiatives on AI:History and Main Fwww.dka.global50Innovation EyeLab to the Market Development AreaFoster an innovative economy by accelerating AI-related research Human Capital AreaProvide AI-skilled employees for a new jobs marketNetworking Area Attract foreign talents,enhance collaborations on AI between academia,industry,and government.Regulation AreaEthical policy on AI-driven implementationsInfrastructure AreaImplement huge updates to the UKs infrastructure12345 UK Strategic Policy Areas of the AI Sector The UK is making great efforts to maintain its current position as the#3 international AI leader behind the USA and China,and to optimise its potential to climb further up the ladder of the Global AI Race.By PwCs estimation,AI technologies are set to contribute 11.9T to the world economy by 2030,and to increase UK GDP by up to 10.3%by 2030.In order to convert this potential into reality,the UK government has formulated strategic industry development initiatives in five key areas(following the EU member states monitoring scheme):Human Capital,Lab to the Market Developments,Networking,Regulation and Infrastructure.Human capital policy targets the UK educational system to create a supply of AI specialists that meets the nations demands.The UK has initiated a National Retraining Scheme to encourage lifelong learning for adults and let them enter new niches in the workforce.The Lab to the market development policy area focuses on accelerating the development of AI-related technologies and the journey from prototype to market-ready products and services.To hasten product development lifecycles,the UK government has committed substantial investments to research programs focused on data science and AI(300 million)as well as sponsoring institutions that deal with AI-related investigations.Among them are the Alan Turing Institute and the recently established Centre for Industrial Digitisation,Robotics and Automation.Meanwhile,networking policies aim to attract highly skilled workers for simplified immigration paths.As part of its networking development initiative,the UK has established the AI Council,an independent expert committee that facilitates collaboration between experts in AI technologies in academia,industry,and Government institutions.In order to develop regulations for data-based technologies,including AI,the UK has also established a Centre for Data Ethics and Innovation(CDEI).The CDEI advises the government on risks and opportunities relating to the adoption of AI and data use in the UK and has power to influence policy-making.Finally,infrastructure policies focus on supporting optimal technology environments that affect the development of new technology implementation.As part of this fifth development area,the UK has made large investments in 5G,full-fibre networks,and transportation projects.Sources:jrc.ec.europa,gov.uk(AI Watch:National Strategies on Artificial Intelligence,European Commission 2019),parliament.ukGovernment Initiatives on AI in 2019-2021 51Innovation EyeHuman Capital Transformation1AI Main Trends:Human Capital TransformationExamples of Government InitiativesThe UK is investing heavily in the creation of new training and educational opportunities centered on AI in order to increase the number of AI specialists,technologists,and technicians.This is to help the nations supply of AI expertise meet actual real-world demands.Additionally,these efforts are also partly aimed at increasing demand for AI specialists,given that supply also,to some extent,influences demand,and a larger quantity of AI specialists available helps to create jobs where their services will be needed.A wide range of AI specialties have been created throughout UK universities,coupled with specific recommendations relating to staff training programs within businesses(and across a large number of industries).In order to help accommodate qualifications and skill sets to match market needs,a number of funding initiatives for educational,research degree programs and industry placements in data science companies have been launched in the UK.These include the AI Turing Fellowships,funding in foster skills in STEM areas(406 million),and industry-funding for new AI Masters places.Meanwhile,the UK governments National Retraining Scheme focuses on requalification programs for employees to help create a new wave of Data Science professionals,as well as to increase the overall earning power of the UK populace.Teacher Development funding(42 million)AI Turing FellowshipsNational Retraining Scheme16 New Centres for Doctoral TrainingFunding in foster skills in STEM areas (406 million)Industry-funding for new AI Masters places Sources:jrc.ec.europa.eu52Innovation EyeAI Main Trends:Acceleration of AI-Driven R&DExamples of Government InitiativesAI is one of the most significant drivers of economic growth,productivity,and increases in GDP.Many high-profile reports published within the first half of 2021 predict that the implementation,development,and industrialisation of AI technologies have the potential to boost the UK economy by 20-30%.Meanwhile,the overall volume of investment in the AI Industry in the UK surpasses AI-focused investments in all other EU member states combined across the full scope of the UK investment community,including venture capital,private equity,and M&A.In tandem with these trends,the current COVID-19 pandemic has also given a significant boost to advanced AI techniques such as Machine Learning,which has proved to be the most effective method of monitoring infectious disease generally(and COVID-19 transmission in particular),providing tangible proof of the capacity for AI to rapidly and efficiently tackle real-world issues of great national concern.An Investment Fund of 2.5 billion to help firms to adjust to innovative business modelsFunding of Alan Turing Institute(42 million in the period 2015-2020)Launch of centres of medical imaging and digital pathology using AIFunding for research in data science and AI(300 million)179 AI grants in AI Research Area(157 million)AI programmes on engineering,urban planning,and healthcare(79 million)Robotics and AI in Extreme Environments Programme(93 million)(part of Industrial Strategy Challenge Fund)50 million in funding for launching of a Centre for Industrial Digitisation,Robotics and AutomationSources:jrc.ec.europa.euAcceleration of AI-Driven Research and Development253Innovation EyeAI Main Trends:Intensification of Networking Examples of Government InitiativesAI training and business sustainable partnerships.The large-scale implementation and industrialisation of AI requires specific infrastructural support mechanisms.It also requires the long-term development of optimally structured and sustainable economic,policy,and industrial ecosystems in order to evolve in an effective manner.This includes bottom-up resources,such as educational systems capable of educating large numbers of AI specialists and specific systems that facilitate and incentivise strategic partnerships and cross-sector,cross-domain partnerships between industry and academia,product and service providers,and individual AI companies themselves.The essential role of development and monitoring institutions in creating and maintaining AI leadership potential.Creating specialised institutions dedicated to formulating and executing AI policy;recommendations;cross-sector and public-private sector partnerships and collaborations;dialogue between Government,industry,and academia;and other forms of guidance,regulatory infrastructure,and systems for facilitating cooperation among different stakeholders are pivotal to both establishing and maintaining international AI leadership positions,and the UK is an excellent proof of concept for this.Providing simplified migration regulations to attract foreign talent in Data Science is a fast way to fulfil lacks and lags in domestic AI specialists in the UK.While educational reforms and requalification programmes take time to foster their actual results,Exceptional Talent visas(of which up to 2,000 are given per year)present a very efficient solution to solving gaps between labour market supply and demand for Data Scientists and Researchers.Simplified migration rules for leading scientists and researchersEstablishment of the AI Council,which facilitates dialogue between academia,industry,government(AI policymakers),and the publicIncreasing the amount of Exceptional Talent visas(up to 2,000 per year)Creation of data-driven hubs for example,a Bayes centre for data science and AI(30 million)in EdinburghSources:jrc.ec.europa.euIntensification of Networking 354Innovation EyeAI Main Trends:Focus on AI Policy ImplementationExamples of Government InitiativesAI technologies must be used in an ethical manner.Mandatory public disclosure of how new technologies may affect human lives is expected to build a high degree of public trust in the AI sector.AI decision-making should include human involvement.Decision-making processes that fail to include public engagement and feedback can quickly lead to large losses in public confidence.The establishment of ethical standards is essential for confidence-building.Implementing clear ethical standards around AI may accelerate rather than delay the adoption of new technologies among public officials and service users.AI-related human rights violations must be avoided.AI-related privacy concerns are becoming increasingly important as the potential for technology to facilitate intrusions on privacy continues to increase.The AI Industry may need additional,tailored,sector-specific guidance on minimum and best-practice ethical principles relating to human rights in relation to AI in general,and privacy protection in an increasingly technologically sophisticated world in particular.Successful AI governance requires clear and transparent legal frameworks.Transparent,sensible,and easy-to-understand regulations serve to establish proper controls for understanding,managing,and mitigating risks in order to achieve better AI governance.Establishing the Centre for Data Ethics and Innovation(CDEI)to offer advice for the safe,sustainable,and ethical use of AIStrengthening the Data Protection Act to determine the rules for collection,storage,and usepersonal data Sources:jrc.ec.europa.eu Focus on AI Policy Implementation455Innovation EyeAI Main Trends:Infrastructure UpdatesExamples of Government InitiativesEnhancing and upgrading foundational instructure that supports the UK AI Industry is fundamental to establishing data-driven AI leadership positions.5G upgrades to standard and full fibre networks as an element of digital infrastructure enhancements will give both citizens and institutions access to high-speed data-exchange systems,and will facilitate ancillary upgrades to other processes such as data collection.Investments by the UK government of 176 million in 5G technology and 200 million for full-fibre networks are just two examples of how supporting upgrades to the technological infrastructure supporting the UKs AI Industry is critical to maintaining a competitive international edge in the global AI race.The UK has also seen the establishment of a 1.7 billion transportation infrastructure fund to improve intra-city and city-region commuting and transportation.In order to further improve digital infrastructure and facilitate secure data sharing without violating personal rights and privacy laws,while simultaneously simplifying the availability and quality of data,the UK government has invested in its Open Data Institute and the Open Data Research Forum.Both of these initiatives are focused on cultivating collaboration and partnership networks between business,government,academia and the public.The Open Data Institute also provides courses on data science,conducts its own internal research,supports the conversion of data science into tangible social impacts.New Transforming Cities fund to upgrade intra-city transport connections(1.7 billion)176 million in funding for 5G and 200 million for full-fibre networksThe establishment of the Geospatial Commission to improve access to geospatial data for the publicAs a part of data infrastructure,UK government is funding the Open Data Institute and the Open Data Research ForumSources:jrc.ec.europa.eu Infrastructure Updates556Innovation EyeGovernment Initiatives on AI:Summary Focus on AI policy implementationEstablishment of Centre for Data Ethics and Innovation(CDEI)to advise on the safe,sustainable,and ethical use of AIStrengthening the Data Protection Act to determine the rules for collection,storage,and use personal data Intensification of Networking Simplifying immigration rules for leading scientists and researchersEstablishing the AI Council,which facilitates dialogue between academia,industry,Government(AI policymakers),and the publicIncreasing the number of Exceptional Talent visas(up to 2,000 per year)Creating data-driven hubs for example,a Bayes centre for data science and AI(30 million)in EdinburghAcceleration of AI-Driven Research and DevelopmentAn Investment Fund of 2.5 billion to help firms to adjust to innovative business modelsFunding of Alan Turing Institute(42 million in the period 2015-2020)Launch of centres of medical imaging and digital pathology using AIFunding for research in data science and AI(300 million)179 AI grants in the AI Research Area(157 million)AI programmes on engineering,urban planning,and healthcare(79 million)Robotics and AI funding in extreme environments programme(93 million)50 million in funding for the launching of a Centre for Industrial Digitisation,Robotics and AutomationHuman Capital TransformationTeacher Development funding(42 million)AI Turing FellowshipsNational Retraining Scheme16 New Centres for Doctoral TrainingFunding in foster skills in STEM areas(406 million)Industry-funding for new AI Masters places Infrastructure updatesNew Transforming Cities fund to upgrade intra-city and transport connections(1.7 billion)176 million for 5G and 200 million for full-fibre networksEstablishing the Geospatial Commission to improve access to geospatial data for publicAs a part of data infrastructure,UK government is funding the Open Data Institute and the Open Data Research ForumSources:jrc.ec.europa,gov.uk,ed.ac.uk,hdruck57Innovation EyeThe Three Main Policy and Ethics Advisory Organisations in the UKAn independent advisory body set up and assigned by the UK government Aims to connect policymakers,industry,civil society,and the public to develop the right regime for data-driven technologiesJoint BEIS-DCMS unit responsible for controlling the implementation of the AI and Data Grand ChallengeResponsible for driving the implementation of AI technologies for the benefit of everyone in the UKUKs Data Ethics Framework The Data Ethics Framework aims to guide the appropriate and responsible use of data in the public sector.Understanding a users needs and what is in the public interest.1Centre for Data Ethics and InnovationOffice for Artificial Intelligence Artificial Intelligence CouncilAn independent expert committee set up to advise the government and high-level stakeholders in the AI ecosystemAims to support and promote the growth of AI adoption and use in businesses and societyEnsuring consistent practice,working within existing skill sets while designing data-driven projects.5Understanding the relevant laws and codes of practice for addressing those needs.2Making work transparent and accountable.6Discolsing personal data only much as is necessary.3Ensuring the appropriate and responsible use of data.7Understanding the limits of data applicability and the appropriateness of its use.4Sources:gov.uk.ai.ethics;gov.uk.ai.public;gov.uk.cdei.ai;gov.uk.ai.council;gov.uk.data.ethics Government Initiatives on AI:SummaryArtificial Intelligence Rwww.dka.global59Innovation EyeUK AI Council:AI RoadmapIn recent years,the UK has been the home of a large number of initiatives and start-ups aimed at developing and deploying AI across the economy for the benefit of society.Following the COVID-19 pandemic,AI will be integral to tackling the major challenges of rebuilding and leveling the UK economy by creating jobs and prosperity beyond London and the South East,providing new forms of health and social care,achieving net-zero carbon emissions,and ensuring resilience to future economic,health,and environmental shocks.AI will also create completely new opportunities for humanity to flourish.The UK has earned a place among global leaders in many areas of AI,from accelerating drug discovery to helping businesses factor climate volatility into their decisions.As governments around the world have already established and financed national plans for cross-sector AI application and financed them,the UK government is now also being called for action.In 2021,the UK AI Council,an independent AI expert committee that provides advice to the UK Government for building a national AI ecosystem,published an independent report on building an AI Roadmap for the UK.In this report,four pillars on which to build the UKs future in AI are described.It invites action across government to keep the UK at the forefront of safe and responsible AI.It emphasises the importance of doubling down on recent investment the UK has made in AI,while at the same time shifting the efforts on integrating existing approaches to ethics,security,and social impacts.The strategy must be carefully designed,building on the countrys strengths.With this purpose,the following page details the recommendations given by UK AI Council.R&D and InnovationSkills and DiversityData,Infrastructure and Public TrustNational,Cross-sector AdoptionDirections of Opening AI PotentialSources:gov.uk60Innovation EyeAI Roadmap:Research,Development,and Innovation2311.Scaling Up:Make sustainable public sector investments in AIEnsure consistent access to top talent from around the worldFind new ways to bring researchers,disciplines,and sectors togetherProvide AI-skilled employees for a new jobs market2.The Alan Turing Institute as a Truly National Institute:Move from local leadership to globalProvide assured long-term public sector funding that will give the Turing Institute and others the confidence to plan and invest in strategic leadership for the UK in AI research,development,and innovation 3.Ensure Moonshots:Ensure challenge-led,high-risk,scalable programmes that are both advancing and leveraging AITackling fundamental challenges such as creating explainable AI or important goals in any area where AI can contribute stronglySources:gov.uk61Innovation EyeAI Roadmap:Skills&DiversityThis would include research fellowships,AI-relevant PhDs across disciplines,industry-led Masters courses,and level 7 apprenticeships.10-Year Programme of High Level AI SkillbuildingThe public needs to understand the risks and rewards of AI so they can become confident and informed users.An online academy for understanding AI,with trusted materials and initiatives,would support teachers,school pupils,and the public in lifelong learning about AI.Commit to Achieving AI and Data Literacy for the PublicIt was suggested to benchmark and forensically track levels of diversity to make data-led decisions about where to invest.This will also ensure that underrepresented groups are given equal opportunity and included in all programs.Make Diversity and Inclusion a PrioritySources:gov.uk62Innovation EyeAI Roadmap:Data,Infrastructure,and Public TrustThe goal:Invest in the relevant organisations,link general principles to specific applications,and pursue initiatives for pump priming innovation and enabling safe data sharing for valuable usesConsolidate and accelerate the infrastructure needed to increase access to data for AIThe goal:The UK should lead in developing appropriate standards to frame the future governance of data.Lead the development of data governance options and their usesThe goal:The UK must lead in finding ways to enable public scrutiny of,and input into,automated decision-making and help ensure that the public can trust AI.Ensure public trust through public scrutinyThe goal:Building on its strengths,the UK has a crucial opportunity to become a world leader in good governance,standards,and frameworks for AI and enhance bilateral cooperation with key actors.Positioning the UK with respect to other major AI nationsSources:gov.uk63Innovation EyeAI Roadmap:Digital Twins ProgrammeCurrently in development by the Centre for Digital Built Britain,with the end goal of creating an Information Management Framework.The Digital Twin Programme would force the extensive exploitation of open synthetic data in order to expose complex and hidden risks in simulated environments prior to the implementation of AI systems in a real life.Extensive exploitation of open synthetic data in order to expose complex and hidden risks in simulated environments before the implementation of AI systems in real life.Enabling fast and safe development,testing,and demonstration in a simulated environment will provide an ability to gather all necessary information for the regulatory decision.Exposure of Complex Risks in a Simulated EnvironmentRealistic representation of real-life physical assets,processes,artifacts(e.g.,documents),and interactions in a simulated environment are recognised as the key novel innovation for a fast,accurate,reliable,and scrutinised approach for modeling the impact of developed AI systems.Digital Twin as a Real-Life Laboratory Sources:gov.uk The National Digital Twin Programme64Innovation EyeAI Roadmap:National,Cross-Sector AdoptionIncrease buyer confidence and AI capability across all sectors and all sizes of companyUse AI to meet the challenges of Net Zero carbon emissionsEnable robust public sector investments in AIUse AI to help keep the country safe and secureSupport the UKs AI start-up vendor communityLead the way in using AI to improve outcomes and create value in healthcareSources:gov.uk65Innovation EyeAI Policies and EthicsThree Main AI Policy and Ethics Advisory Organisations in the UKAn independent advisory body set up and assigned by the UK government Aims to bring together policymakers,industries,civil society,and the public to develop the right regime for data-driven technologiesJoint BEIS-DCMS unit responsible for controlling the implementation of the AI and Data Grand ChallengeContribute and drive the implementation of AI technologies for the benefit of everyone in the UKUKs Data Ethics Framework The Data Ethics Framework aims to guide the appropriate and responsible use of data in the public sector.Understanding the public interest and users needs.1Centre for Data Ethics and InnovationOffice for Artificial Intelligence Artificial Intelligence CouncilAn independent expert committee set up to advise the government and high-level stakeholders of the AI ecosystemSupport and promote the growth of AI adoption and use in businesses and societyEnsuring consistent practice,working within existing skill sets while designing data-driven projects.5Understanding the relevant laws and codes of practice for addressing those needs.2Making work transparent and showing accountability.6Disclosing personal data only to the extent that is necessary.3Ensuring the appropriate and responsible use of data.7Understanding the limits of data applicability and the appropriateness of its use.4Sources:gov.uk.ai.ethics;gov.uk.ai.public;gov.uk.cdei.ai;gov.uk.ai.council;gov.uk.data.ethics AI Policies and Ethics:COVID-19,Facial Recognition,and Ewww.dka.global67Innovation EyeAI adoption to combat COVID-19AI adoption has garnered special consideration in our efforts to combat COVID-19.AI can:Assist in preventing or slowing the spread of COVID-19 through surveillance and contact tracing.(AI for analytics,Blockchain for tracing).Detect and diagnose the virus and predict its evolution.Respond to the health crisis through providing personalised information(Precision Healthcare).Monitor recovery and improve early warning tools.Help researchers to understand the virus and accelerate medical research on drugs and treatments.Health Data Policy FrameworkAI policy needs to support two segments:1)data policy;and 2)mechanisms such as mobile applications,which can feed into and monitor citizens data.Good AI policy relies heavily on the following:Cross-border(international)data infrastructureFair and responsible use of behavioural dataTrust on the part of the public that their data will be used fairly and anonymously.An independent oversight body(a watchdog-type organisation),responsible for the fair and ethical application of any data-driven public health measuresTowards a Policy Framework for AI to Combat COVID-19 Sources:All Party Parliamentary Group on Artificial Intelligence(APPG AI)(May 2020)Public Health:How can AI help in the fight against COVID-19?Parliamentary Brief,Big Innovation CentreAll Party Parliamentary Group on Blockchain(APPG AI)(June 2020):How can Blockchain help in the time of Covid-19?Parliamentary Brief,Big Innovation CentreBig Innovation Centre and Deep Knowledge Analytics(November 2020):Global mHealth Industry Landscape Overview,Innovation Eye https:/ Health(mHealth)applications:Current trendsCOVID-19 has reshaped the possibility of mHealth as a policy initiative.mHealth refers to personal health care over mobile phone applications(Apps)or wearable technology.The technological sophistication necessary for mHealth is steadily rising for mHealth possibilities,with the adoption of digital and AI technology combined with mobile phone penetration.There is increased diversification in mHeath applications covering a range of areas from symptom tracing,health assistants(advice),and pandemic spread(track and trace).Government policies worldwide are using mHealth applications to control the current COVID-19 pandemic.68Innovation EyeArtificial Intelligence has the potential to accelerate scientific discoveries through faster data processing.The scientific and research communities hope to fill gaps in their understanding of COVID-19 by utilising intelligent AI algorithms and Machine Learning.The usual pace of of drug development is too slow to meet the challenges of the COVID-19 pandemic,and thus BenevolentAI,a UK Machine Learning-based drug discovery platform,has been developed to rapidly identify already existing drugs that demonstrate anti-COVID activity.This platform identifies promising candidates that may inhibit COVID-19 infection.These include Baricitinib,a drug available on the market and approved for rheumatoid arthritis.Baricitinib is now in the late stage of clinical trials as a potential treatment of COVID-19.Another essential benefit of AI-based algorithms during the pandemic has been AgriTech developments that increase food security.The pandemic situation has revealed a weakness in food supply chains worldwide and in the UK.Start-up Mantle Labs is aiming to help UK farmers and retailers improve crop-monitoring by AI-based analysis of satellite images of farmlands.Mobile applications have rapidly taken on a pivotal role in public health management.For example,the Babylon app,developed by the UK company Babylon Health,is an AI-based alternative chatbot acting as a medical helpline.It aims to reduce pressure on healthcare helpline systems as well as provide more accurate patient diagnosis.This app performs a range of TeleHealth functions:automotive symptom checker,fully-qualified consultant therapist,and so on.In 2020,the NHS also launched a contact tracing app,while the COVID Symptom Study app,an epidemiological research app,helps shed light on the spread of the virus and explores ways to fight the pandemic.PAnalysis of virus spread and mutation rateIdentifies people with severe risk of complicationsAdvanced diagnosticsSupply chains during COVID-19:AgriTech for crop-monitoring solution for food securityTeleHealth:reducing overload on the healthcare systemMassive screening of research paper data setDrug discovery and developmentPattern analysis of medical imaging for early diagnosticsPublic health managementCase Examples:Use of AI to Combat COVID-19Quarantine enforcement Prediction of the evolution of the pandemicSources:weforum,internetofbusiness,nature,mit.tech.review.ai-triage-covid,APPG AI Evidence SessionsAI-Driven Applications Solutions to Fight COVID-19Solutions for organisations to scale and adjustAccelerate researchand treatment69Innovation EyeAI algorithms in COVID apps have various uses in public health management.They monitor,and potentially slow the spread of the virus through the population by tracing peoples contacts(Contact tracing apps).They allow users to self-diagnose for covid symptoms(Self diagnostics).They track virus mutation rate,and a range of other symptoms as well(Medical recording).They provide tools for keeping populations secure from potentially infected individuals(Quarantine enforcement).Alerting apps and Information apps,a resource provided by government and health organization officials,allow citizens an overview of the COVID19 pandemic landscape.The following infographic data contains a statistical breakdown of app production by country.Those countries leading app production have made 3 covid apps including the UK(2 covid apps).Those covid apps are developed exclusively for users who are not medical or NHS workers,and are primarily oriented on the citizens of the following countries:Australia(3 apps),Canada(3 apps),France(3 apps),Germany(3 apps),India(10 apps),Italy(6 apps),South Korea(3 apps),Spain(3 apps),United Kingdom(2 apps),and United States(8 apps).The government is a leading COVID apps producer for the countries indicated.In the United Kingdom half of COVID app producers are private developers.At the date depicted,the United States remains outside the government dominance trend of app development and 75%of their COVID apps are made by Multi-stakeholders.Contract tracing is the predominant type of COVID-related apps for each of the listed countries.UK has a pretty low number of COVID-oriented apps by number,and by specialization.Howhere,this statistic excludes TeleHealth apps,which are a generally useful tool in cases of coronavirus infection.Case Examples:AI-based COVID-19 Apps GovernmentalPrivateMultistakeholderInformationMedical ReportingSelf DiagnosticContact TracingQuarantine EnforcementAlertingSources:oe.int;tableau.publi,Health Innovation Eye 202070Innovation EyeCitizen participation by designNot discriminate against citizensPOLICY FRAMEWORK:Facial recognition for national securityTransparency and accountabilityData governance on collection,storage and use of dataPurposeful useWatchdog on technology application and useTowards a Policy Framework for AI in Facial Recognition for National SecurityAll Party Parliamentary Group on Artificial Intelligence(APPG AI)(July 2020)Face and Emotion Recognition Technologies:How can regulation protect citizens and their privacy?Parliamentary Brief,Big Innovation CentreBig Innovation Centre(2020):Will Face and Emotion Recognition change the UK?At the UK Political Party Conferences,Big Innovation CentreAndersen,Birgitte(2022)Public Policy and Government,in Kerrigan,Charles(ed)Artificial Intelligence,Law and Regulation,Edward Elgars Centre for Data Ethics and Innovation(CDEI)(November 2020)Review into Bias in Algorithmic Decision-Making,CDEIClement-Jones,Tim(March 2020):The Potential Role of GovTech and Its Governance(March 2020),Provocation,APPG AI and Big Innovation CentreThe British Security Industry Association(BSIA)(February 2021):Automated Facial Recognition:A guide to ethical and legal use,The British Security Industry Association(BSIA)Government Communications Headquarters(GCHQ)(2021):Pioneering a New National Security:The Ethics of Artificial Intelligence.Artificial Intelligence at GCHQ.National Security Commission on Artificial Intelligence(2021):Final Report.National Security Commission on Artificial Intelligence.The Surveillance Camera Commissioner(December 2020)-Facing the camera-the guidance is for forces to follow when considering the deployment of Live Facial Recognition(LFR)surveillance camera technology.A Guide to Deliver Effective and Ethical Use of AI in Facial Recognition for National SecuritySummary of published guidancesPolicy discussions have touched upon a variety of approaches,but it has become very clear that facial recognition deployment must guarantee data protection,not discriminate,and be used responsibly.A policy framework should(i)ensure the quality and applicability of data sets used for the training of facial recognition technologies,(ii)regulate audits and compliance checks,and(iii)outline rules for the collection,processing,and storage of citizens biometric data for public and commercial use.Transparency(enforced by a watchdog)and accountability is vitral for risk mitigation.Sources71Innovation EyeTowards a Policy Framework for AI in Education and SchoolsSources:All Party Parliamentary Group on Artificial Intelligence(APPG AI)(October 2020)AI in Education:Embedding AI tools into teaching curricula.Parliamentary Brief,Big Innovation CentreAll Party Parliamentary Group on Artificial Intelligence(APPG AI)(November 2020)AI in Education:Designing fair and robust AI-based assessments systems.Parliamentary Brief,Big Innovation CentreBig Innovation Centre,APPG AI and KPMG(2018):Learning to Learn:The Future-Proof Skill,Big Innovation CentreAndersen,Birgitte(2022)Public Policy and Government,in Kerrigan,Charles(ed)Artificial Intelligence,Law and Regulation,Edward Elgars DCMS Department of Education(January 2021):Skills for Jobs:Lifelong Learning for Opportunity and Growth.London,Department of EducationA Guide to Delivering Effective and Ethical Use of AI in Education and SchoolsSummary of published guidancesAI offers a new approach to learning methods,where interdisciplinarity and problem-solving is at the heart of learning environments.This will integrate soft skills(creative)and hard STEM(science,technology,engineering,and mathematics)skills,and enhance the experience and purpose of education.Emerging technology can also offer a new approach to student assessment,feedback,and performance.AI has the capacity to improve each of these through the interactive nature of technology.AI also offers opportunities for precision learning and for each student to follow their interests and skills.This can also free up time for teachers to foster class community.To ensure that software is developed to fit their purpose,educators must be involved in the shaping of AI tools.However,for the adoption of AI in education to be successful,policy must also focus on train the trainers to enhance the successful and speedy transformation of schools.Physical barriers to adoption,including access,physical infrastructure,and costs should be assessed and categorised,so that an infrastructure investment plan can be prepared for policy.Success requires that learners,teachers,assessment and teaching material,and the physical environment are futureproof.A Guide to Deliver Effective and Ethical Use of AI Education and SchoolsSummary of published guidancesProvide all school children with computers for AI-supported education POLICY FRAMEWORK:AI adoption in education and schoolsTrain the trainers on AI adoption,opportunities,and risksCreate precision learning and free up time to foster class communitySafely deploy AI-based assessment systemsInvolve educators in the software shaping of AI toolsPut interdisciplinari-ty and problem-solving at the heart New subjectsAll Party Parliamentary Group on Artificial Intelligence(APPG AI)(October 2020)AI in Education:Embedding AI tools into teaching curricula.Parliamentary Brief,Big Innovation CentreAll Party Parliamentary Group on Artificial Intelligence(APPG AI)(November 2020)AI in Education:Designing fair and robust AI-based assessments systems.Parliamentary Brief,Big Innovation CentreBig Innovation Centre,APPG AI and KPMG(2018):Learning to Learn:The Future-Proof Skill,Big Innovation CentreAndersen,Birgitte(2022)Public Policy and Government,in Kerrigan,Charles(ed)Artificial Intelligence,Law and Regulation,Edward Elgars DCMS Department of Education(January 2021):Skills for Jobs:Lifelong Learning for Opportunity and Growth.London,Department of EducationSourcesAI in the UK:Prominent Reports on AI(2019-2021)www.dka.global73Innovation EyeAI in the UK:Prominent Reports on AI(2019-2020)Date:2019Name:Understanding Artificial Intelligence Ethics and SafetyEditors:The Alan Turing Institute,Office for Artificial Intelligence,Government Digital ServiceDate:March 2020Name:The Role of Emerging Technology in Transforming Government in the UKBy Lord Clement-Jones CBEEditors:Provocation,APPG AI,and Big Innovation CentreDate:July 2020Name:Guidance on AI and Data ProtectionEditors:Information Commissioners Office(ICO)Date:November 2020Name:Facing the Camera:the Protection of Freedoms Act 2012&the Surveillance Camera Code of PracticeEditors:The Surveillance Camera CommissionerDate:2020Name:The Aletheia FrameworkEditors:Rolls Royce74Innovation EyeAI in the UK:Prominent Reports on AI(2020-2021)Date:November 2020Name:Review Into Bias in Algorithmic Decision-MakingEditors:Centre for Data Ethics and InnovationDate:December 2020Name:AI in the UK:No Room for ComplacencyEditors:House of Lords Liaison CommitteeDate:January 2021Name:UK AI Council:AI RoadmapEditors:The UK AI CouncilDate:January 2021Name:Skills for Jobs:Lifelong Learning for Opportunity and GrowthEditors:Department for Digital,Culture,Media and Sport(DCMS)Date:February 2021Editors:The British Security Industry Association(BSIA)Name:Automated Facial Recognition:a Guide to Ethical and Legal Use75Innovation EyeAI in the UK:Prominent Reports on AI(2021)Date:2021Name:Pioneering a New National Security:the Ethics of Artificial IntelligenceEditors:Government Communications Headquarters(GCHQ)Date:February 2021Name:Government Response to the House of Lords Select Committee on Artificial IntelligenceEditors:Department for Digital,Culture,Media and Sport(DCMS)Date:March 2021Name:Regulating Artificial Intelligence:Where Are We Now?Where Are We Heading?Editors:Technologys Legal Edge(online)Date:February 2021Name:The CMAs Digital Markets Strategy:February 2021 RefreshEditors:Competition&Markets Authority(CMA)Date:2021Name:Algorithms:How They Can Reduce Competition and Harm ConsumersEditors:Competition and Markets Authority(CMA)76Innovation EyeDate:2021Name:Artificial Intelligence,Human Rights,Democracy and the Rule of LawEditors:The Council of Europe,The Al and Turing InstituteAI in the UK:Prominent Reports on AI(2021)Date:2021Name:Final ReportEditors:National Security Commission on Artificial IntelligenceDate:2021Name:Data Governance in the Post-Brexit Era:Is the National Data Strategy Ambitious Enough?Editors:The Open Data InstituteDate:2021Name:Transforming Our World With AI:UKRIs role in Embracing the OpportunityEditors:UK Research and InnovationDate:2021Name:Centre for Applied Data Ethics Strategy:Enabling Ethically AppropriateResearch and Statistics for the Public GoodEditors:UK Statistics Authority77Innovation EyeProminent UK Reports/Books on AI(2020-2021)Date:2021Name:Data Alchemy:the Genesis of Business Valueby Tirath Virdee and Doug BrownPublisher:Lid Publishing Date:2020 Name:The Reasonable Robot:Artificial Intelligence and the Lawby Ryan AbbottEditors:Cambridge University PressDate:September 2021Name:National AI StrategyEditors:HM Government Date:May 2021Name:Understanding the UK AI Labour Market:2020Editors:Ipsos MORIDate:March 2021Name:The Government Report on Transparency Reporting in Relation to Online Harms Editors:HM GovernmentDate:December 2021Name:Online Harms White Paper:Full Government Response to the ConsultationEditors:HM Government78Innovation EyeProminent Non-UK Reports on AI(2019-2021)Date:December 2020Name:Ad Hoc Committee on Artificial Intelligence(CAHAI):Feasibility StudyEditors:EU Council of EuropeDate:March 2021Name:AI Advocates Seek Vast Expansion of New National InitiativeEditors:Science Policy News from AIPDate:June 2019Name:The Age of Digital InterdependenceEditors:UN Secretary-Generals High-Level Panel on Digital CooperationDate:November 2020Name:A First Look at the OECDs Framework for the Classification of AI Systems,Designed to Give Policymakers ClarityEditors:OECD.AI Policy Observatory Date:2021Name:Global AI action allianceEditors:World Economic Forum Date:January 2020Name:The Role of Artificial Intelligence in Achieving the Sustainable Development GoalsEditors:Nature CommunicationsDate:2021Name:State of Implementation of the OECD AI Principles:Insights From National AI PoliciesEditors:OECD Digital Papers 79Innovation EyeARTIFICIAL INTELLIGENCELaw and Regulation EDITED BY CHARLES KERRIGANPublisher:Edward ElgarsForthcoming March 2022c 496 pagesCONTRIBUTIONSUnderstanding AI(by Tirath Virdee)Corporate Governance(by Martin Petrin)Regulation(by Hannah Yee-Fen Lim)Commercial Contracts(by Iain Sheridan)Commercial Trade(by Minesh Tanna and William Dunning)Agency and Liability(by Jason G Allen)Data and Data Protection(by Peter Church&Richard Cumbley)Competition Law(by Suzanne Rab)Intellectual Property(by Rachel Free)Employment(by Dana Denis Smith)Disputes and Litigation(by Vanessa Whitman and Kushal Gandhi)Financial Services(by Richard Hay&Sophia Le-Vesconte)Insurance(by Stephen Kenny)Retail and Consumer(by Matthew Bennett)Healthcare(by Roland Wiring)Telecoms and Connectivity(by Suzanne Rab,Serle Court)Real Estate(by Nick Doffman,Nick Kirby&Alastair Moore)Ethics(by Trish Shaw)Bias and Discrimination(by Minesh Tanna and William Dunning)Public Policy and Government(by Birgitte Andersen)Education(by Stefano Barazza)Taxonomy of AI(by Tirath Virdee)Automation and Fairness(by Emre Kazim,Jeremy Barnett,Adriano Koshiyama)Risk Management(by Stephen Ashurst)Business Models and Procurement(by Petko Karamotchev)Explainable AI and Responsible AI(by Oliver Vercoe and Charles Kerrigan)Legaltech(by Richard Tromans)AI Community ContributionsSeries of APPG AI Policy Briefs:Selected reports and articles:Data Governance:Beyond GDPR Face and Emotion Recognition Technologies AI in Public Health 2022-23 ProgrammeCwww.dka.global81Innovation EyeConclusion The UK has a virtuous circle of growth in AI development,investment,deployment,and adoption.NEW FINANCE:Investment confidence in the UK and London is high and growing,and the UKs AI innovation and investment ecosystem is becoming a magnet for entrepreneurial finance.In addition,the UKs high-tech AI fintech companies are located in its own financial centre of London that are needed to grow it.NEW BUSINESS BRANDS:As Europes marketing and advertising hub,London is now also a hub for the innovation of new AI marketing and advertising businesses for AI development,deployment,and adoption.MORE TALENT:The UK has a highly integrated AI innovation ecosystem of talent including an AI science base,a technological foundation,and a tradition of digital entrepreneurship.These industry,finance,and talent systems are mixed with a talent-enabling network of AI think tanks,hubs,doctoral training centres,and events companies that are turning the UK into a world-leading AI community.ENGAGED REGULATORS:The UKs AI entrepreneurship,talent,investment,and branded businesses are in close proximity with regulators,which are increasingly playing a pivotal role in the promotion of dialogue and the shaping of the AI Industry(in discussion with firms)to become ethical and purposeful.Examples of institutions and strategies include the Centre for Data Ethics and Innovation(CDEI),the Office for AI,the All-Party Parliamentary Group on AI,and the AI Strategy published this autumn,to name a few.Overall,we can see how AI entrepreneurship activity in the UK is located close to finance,marketing,and regulatory bodies that are needed to grow it and the AI community.Conclusion82Innovation EyeConclusionKey numbersThe UKs AI funding landscape:The total amount of funding received by the UKs AI Industry to date is 13.8bn,and the total growth in the funding to the UKs AI Industry over the past three years(2019-2021)is 9bn.The total number of investors that have provided this funding now exceeds 1,500,and this number has grown by 900 over the past 3 years(2019-2021).The Top 3 most funded AI sectors are FinTech,Marketing&Advertising,and Health,which together have received ca.60%of total AI funding in the UK.meanwhile,the Top 15 most funded UK AI companies have received ca.26%of the funding provided over the past 3 years(2019-2021).*Funding includes investments,donations,grants,and subsidies.UKs AI Industry Demographics:The total number of AI companies(start-ups and scale-ups)in the UK now exceeds 2,000,which is double that of three years ago,as 1,000 new AI companies have appeared over the past 3 years(2019-2021).Some 65%of the UKs AI start-ups and scale-up companies are headquartered in London.The Top 3 AI sectors by number of companies are Marketing&Advertising,FinTech,and Consulting,which together constitute ca.47%of the UKs total number of AI companies.About Uwww.dka.global84Innovation EyeInnovation Eye was jointly founded in March 2019 by Big Innovation Centre and Deep Knowledge Analytics to provide sophisticated market analytics,industry intelligence,comparative industry classification frameworks,and benchmarking case studies.The company develops advanced tools for analysis and visualization of technology and innovation ecosystems through reports,custom-made consultancy products and services,and a dynamic interactive online IT-platform with the aim of optimising the strategic agendas of international corporations and technocratic governments seeking to implement,stabilise and optimise their global positions in advanced technology-driven industries.Big Innovation Centre has substantial expertise in these areas,having run cross-industry task forces since 2011 on building innovation and investment ecosystems,future proofing corporate businesses models,and being the secretariat company for the UK All-Party Parliamentary Group on Blockchain and the All-Party Parliamentary Group on Artificial Intelligence.Meanwhile,Deep Knowledge Analytics has established itself as the leader of sophisticated DeepTech Industry intelligence and analytics relating to DeepTech sectors including AI,FinTech,and GovTech.By combining AI-driven Big Data analytics with advanced infographic mindmaps and the production of state-of-the-art data visualisation and dynamic data analytics,industry intelligence platforms,Innovation Eye aims to provide multinational corporate and governmental clients with an advanced,user-friendly suite of tools,frameworks,and solutions for formulating,optimising,and stabilising the development and execution plans underlying their strategic interests.In summary,Innovation Eye:implements advanced ecosystem mapping projects relating to interactive online IT-platforms using dynamic infographic mindmaps and smart-matching capabilities for industry stakeholders;provides tangible technological forecasting of advanced tech-driven industries and innovation economies;and informs international corporations and governments on how to become and remain competitive and utilise their resources in a maximally efficient and synergetic manner.About Innovation Eye85Innovation EyeBig Innovation Centre is one of the biggest,best,and most exclusive technology and innovation consultancy networks in the world.Launched in September 2011,the company exists to build a global innovation hub by 2025,create great companies,and make the world more purposeful and inclusive through the enormous potential of technology,creativity,and innovation.Big Innovation Centres technology and innovation consultancy brings together world leaders,regulators,and executives of the worlds biggest companies and key decision-makers to shape the future with Artificial Intelligence,Blockchain,and digital transformation.Twice-awarded the title of Think Tank of the Year by the PRCA,Big Innovation Centre is the founding Secretariat of the UK All-Party Parliamentary Groups on Artificial Intelligence and Blockchain(APPG AI and APPG Blockchain)and is at the centre of mapping global and regulatory trends in these areas.Big Innovation Centre is featured in the prestigious 2020 listing of the Top 5 Digital Transform Consulting/Service Companies in UK by CIO Applications Europe Magazine,and it has received the Greater London Enterprise Award for its communication services.In 2021,the centre became accredited by the Continuous Professional Development(CPD)standardisation body of the UK as professional training provider.Join our AI and Blockchain networks on our Pavilion |Website |Twitter The company has suitable research labs,office and events space plus cutting-edge IT equipment with support,and its spaces are conveniently located in central London next to UK Parliament Square;and in central Riyadh,as well as the business bay of Dubai.Humane TransformationalSustainableExpansiveVALUESWe drive the ways that society will benefit from AI,Blockchain,and digital transformation while managing the risks to social cohesion.AI,Blockchain,and digital are changing the world,and our network is at the forefront of shaping that transformation.Our mission is to ensure that AI,Blockchain,and digital advance the sustainability of our natural,social,and cultural environments.We think big.The global benefits of AI,Blockchain,and digital are endless.Wherever we find a limit to the benefits of these technologies,we find a way to push beyond.About Big Innovation Centre86Innovation EyeDeep Knowledge Analytics is a DeepTech-focused agency producing advanced analytics on DeepTech and frontier-technology industries.We do this using sophisticated multi-dimensional frameworks and algorithmic methods that combine hundreds of specially-designed and specifically-weighted metrics and parameters to deliver sophisticated market intelligence,pragmatic forecasting,and tangible industry benchmarking.Deep Knowledge Analytics is an analytical subsidiary of Deep Knowledge Group,an international consortium of commercial and nonprofit organisations focused on the synergetic convergence of DeepTech and Frontier Technologies(AI,Longevity,MedTech,FinTech,GovTech),applying progressive data-driven InvestTech solutions with a long-term strategic focus on AI in Healthcare,Longevity,and Precision Health.Deep Knowledge Group aims to achieve a positive impact through the support of progressive technologies for the benefit of humanity via scientific research,investment,entrepreneurship,analytics,and philanthropy.Deep Knowledge Analytics specialises in conducting special case studies and producing advanced industry analytical reports on the topics of Artificial Intelligence,GovTech,Blockchain,FinTech and Invest-Tech.It has produced a number of comprehensive analytical reports in coordination with the UK All-Parties Parliamentary Groups on AI and on Blockchain,including its AI in UK Landscape Overview 2018 and Blockchain in UK Landscape Overview 2018,unprecedented in their scope and length,and collectively more than 3,000 pages.The company has also recently deployed advanced interactive online IT-platforms that feature dynamic mind maps and filterable,customizable databases updated with new industry developments in real-time.Deep Knowledge Analytics will continue to expand the scope,depth and topics covered by its analytical reports on frontier technology-driven industries,with the aim to develop the next iterations of their analytical frameworks with a wider breadth and depth of metrics and overall analytics,to apply efficient methods to cross-sector analysis between different industries,and to apply both existing and new analytical frameworks to the design of the new Invest-Tech solutions(novel investment technologies and strategies relevant for the third decade of the twenty-first century),which is the only relevant way to implement the long-term strategic vision of Deep Knowledge Ventures.Digital UK Ecosystem Dashboard:General OverviewThe Digital UK Ecosystem Dashboard provides the most relevant information regarding the state of different industries in the United Kingdom by region as well as in the country as a whole.The dashboard is divided into five main categories:Digital Ecosystem,COVID-19 Analytics,Government Investment Initiatives and Programs,Longevity Initiatives,and Investment Digest.Our company is building a sophisticated cloud-based engine for advanced market and business intelligence in the pharmaceutical and healthcare industries.It includes a data mining engine,infrastructure for expert data curation,and advanced visualisation dashboards,including mindmaps,knowledge graphs,and 3D visualisations.About Deep Knowledge Analytics87Innovation EyeThe All-Party Parliamentary Group on Artificial Intelligence(APPG AI)functions as the permanent authoritative voice within the UK Parliament on all AI-related matters.Its methods of working include research,round tables,webinars,showcasing,and events.Our AI community(of business leaders,entrepreneurs,investors,academics,politicians,policymakers,and civil society)brings evidence,use cases,and future policy scenarios to the UK Parliament to consider the economic,social,and ethical implications of developing and deploying AI.We have a special focus on policy,business,and society.We elucidate the term Artificial Intelligence;gather evidence to better understand it;assess its impact;and,ultimately,empower decision-makers to engage in policymaking within the sphere of AI.FOCUS AREAS THE UNIVERSE OF AI CONSULTATIONSPOLICY AREASAutomated reasoning and decision-makingAutonomous systemsFace and emotion recognitionMachine LearningMulti-agent systemsNatural language understandingSemantic webSmart living(e.g.,cities)Voice recognitionAI Strategy taskforceData strategy consultationCorporate governance consultationIntellectual Property ConsultationData policy and governance(public,business,personal)Health climate change sustainabilityEducation skills jobs the future of workAccountability and ethicsCitizen participationFace recognition national security cyber crimeInnovation and entrepreneurshipDigital and physical infrastructure*All-Party Parliamentary Groups(APPGs)are informal all-party groups in the UK Parliament.They are run by and for MPs and members of the House of Lords.Big Innovation Centre is the appointed Secretariat and research hub for the APPG on Artificial Intelligence.About APPG AI*88Innovation Eye1.How AI and machine learning are helping to fight COVID-19.World Economic Forum.2.UKs NHS trials AI app as alternative to medical helpline.Internet of business.3.Artificial Intelligence and the control of COVID-19.Council of Europe.4.Covid-19 Apps-Extended dashboard.Tableau public5.Artificial Intelligence in the United Kingdom:Prospects and challenges.McKinsey Global Institute6.Artificial Intelligence and UK National Security.Policy Considerations.Royal United Services Institute for Defence and Security Studies7.Artificial Intelligence and Public Standards.A Review by the Committee on Standards in Public Life.The Committee on Standards in Public Life8.Transforming Paradigms.A Global AI in Financial Services Survey.Cambridge Centre for Alternative Finance(University of Cambridge.Judge Business School)9.A guide to using artificial intelligence in the public sector.Department for Digital,Culture,Media&Sport,Government Digital Service10.Thirtieth British Machine Vision Conference.Cardiff University11.AI in Retail&Advertising Summit.RE Work TEAM12.AI,ML and Analytics Conference.Minds Mastering Machines13.AI Council.Government Communication Service14.Office for Artificial Intelligence.Government Communication Service15.Artificial Intelligence Council.Government Communication Service16.Data ethics and AI guidance landscape.Government Communication Service17.Automation and the future of work.UK Parliament18.AI Watch:National strategies on Artificial Intelligence A European perspective in 2019.European Commission19.National Retraining Scheme.Government Communication Service20.Bayes Centre.The University of Edinburghs Bayes Centre21.National AI Strategy.Office for Artificial Intelligence;Department for Digital,Culture,Media&Sport22.AI-2019 Thirty-ninth SGAI International Conference on Artificial Intelligence.BCS,The chartered Institute for IT23.Understanding artificial intelligence ethics and safety.The Alan Turing Institute 24.Data Ethics Framework.Government Digital Service25.Artificial-intelligence tools aim to tame the coronavirus literature.Nature research journal26.Doctors are using AI to triage COVID-19 patients.The tools may be here to stay.MIT Technology Review27.APPG-AI Events.All Party Parliamentary Group on Artificial Intelligence28.Research network for ethical AI launched in the UK.Ada Lovelace Institute,Arts and Humanities Research Council29.Responsible development of AI.The Institute for Ethical AI&Machine Learning30.A guide to using.Government Communication Service31.The public sector.Government Digital Service,Office for Artificial Intelligence32.Data Ethics Framework.Department for Digital,Culture,Media&Sport33.Understanding artificial intelligence ethics and safety.Government Digital Service,Office for Artificial Intelligence34.CDEI AI Barometer independent report.Department for Digital,Culture,Media&Sport,Centre for Data Ethics and Innovation35.Artificial Intelligence and UK National Security.Royal United Services Institute for Defence and Security Studies36.4 Powerful Examples Of How AI Is Used In The NHS.Bernard Marr&Co37.Thinking on its own.Reform38.Artificial Intelligence and Public Standards A Review by the Committee on Standards in Public Life.The Committee on Standards in Public Life39.Transforming Paradigms.A Global AI in Financial Services Survey.Cambridge Centre for Alternative Finance40
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Artificial Intelligence for Drug DiscoveryLandscape OverviewQ3 2022www.deep-pharma.tech2Deep Pharma IntelligenceAI for Drug Discovery Infographic Summary and Mind Maps3Executive Summary17Application of AI for Advanced R&D23Business Activity:Key Trends28Industry developments:Challenges and Forecasts31Business Activity:Overview33Leading Companies by Amount and Stage of Funding3450 Leading Investors in Pharmaceutical AI 40Big Pharmas Focus on AI48AI in Pharma Publicly Traded Companies57Top Publicly Traded Companies Related to AI-Pharma79AI for Advanced R&D:Applications and Use Cases89Top AI Breakthroughs 2018-202190Computational Methods Used by the Most Advanced AI Companies9615 Notable R&D Use Cases of AI Application in Biopharma104Industry Developments 2020-2022142Key Takeaways148Appendix:List of Entities154Overview of Proprietary Analytics by Deep Pharma Intelligence185Disclaimer190This 135-page Artificial Intelligence for Drug Discovery Landscape Overview Q3 2022 report represents the eleventh issue of market analytics focused on the Artificial Intelligence(AI)application in the pharmaceutical research industry.The primary goal of this series of reports is to give a complete picture of the industry environment in terms of AI usage in drug discovery,clinical research,and other elements of pharmaceutical research and development.This overview highlights recent trends and insights in the form of helpful mind maps and infographics and gauges the performance of prominent players who shape the industrys space and relationships.It can help the reader comprehend what is going on in the sector and potentially predict what will happen next.Since the last edition,data has been significantly updated to reflect the fast-paced market dynamics and an overall increase in pharmaceutical AI investment and business development activities.The listings of AI-biotech businesses,biotech investors,and pharmaceutical organizations have been expanded to reflect the pharmaceutical industrys rising interest in sophisticated data analytics technology.Alongside investment and business trends,the report also provides technical insights into some of the latest AI applications and research achievements.2Deep Pharma IntelligenceTable of ContentsIntroductionClinical DevelopmentData ProcessingEarly Drug DevelopmentEnd-to-end Drug DevelopmentInvestorsAI CompaniesCorporationsDEEP PHARMA INTELLIGENCEPharmaTechCROPreclinical DevelopmentDrug RepurposingArtificial Intelligence for Drug DiscoveryLandscape Overview Q3 2022AI Companies-600Investors-1200Corporations-100Selected Pharma AI Deals AI Companies Pharma CorporationsAI Companies 44Deep Pharma IntelligencePharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.Selected Pharma AI Deals 5Deep Pharma IntelligenceAI Companies Pharma CorporationsAI Companies Pharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.40 Leading Companies in AI for Drug Discovery SectorDeep Pharma IntelligenceAtomwiseAbCelleraAI TherapeuticsBeacon Biosignals3BIGS1A2APharma 234AnimaBiotech5Ardigen6AriaPharmaceuticals7Auransa8910Benevolent AlBergBioage Labs1112Berkeley Lights1314 Biovista15 Black Diamond Therapeutics16 ConcertAl17 Cyclica18 CytoReason19 Deargen20EnvisagenicsExscientiaNuritasDeepGenomics21DeepMindHealth222324Healx25Insillico Medicine26Insitro27Lantern Pharma28Neumora2930PharnextRecursionSchrodinger 3132ReviveMed3334 SensyneHealth35 Silicon36 Standigm37 Turbine38 Valo39 XtalPi40Deep Pharma Intelligence6Comparison of Top-40 Leading AI for Drug Discovery Companies Expertise in Drug Discovery R&DAdvanced AI tools for specific Use CasesAdvanced AI systems with multiple models End-to-end AIExpertise in Drug DiscoveryExpertise in AI7Deep Pharma IntelligenceClinical pipeline(phase 1-2)Validated R&D Use casesand preclinical pipeline8Deep Pharma IntelligenceAlexandria Venture InvestmentsSOSVNational Science FoundationRA Capital ManagementCasdin Capital1Creative Destruction Lab(CDL)234GV5Y Combinator6Perceptive Advisors7Sequoia Capital China8910Merck Global HealthAlumni VenturesForesite Capital1112Khosla Ventures13148VC15DCVC Bio16National Institute of Health17EASME-EU Executive Agency18MassChallenge19T.Rowe Price20DeerfieldF-Prime CapitalSurveyor CapitalSoftBank Vision Fund21Invus222324Redmile Group25DCVC Bio26Founders Fund27IndieBio28Fidelity Management2930Temasek HoldingCormorant Asset ManagementNorthpond Ventures31325Y Capital3334 Obvious Ventures35Andreessen Horowitz36Section 3237Lux Capital38AME Cloud Ventures39Eight Roads Ventures40BlackRockForesite CapitalBiotechnology Value FundLifeforce Capital41Felicis Ventures4344Janus Henderson Investors45Tencent46ARCH Venture Partners47Novo Holdings48Flagship Pioneering49504250 Leading Investors in AI for Drug Discovery Sector600 AI Companies:Regional ProportionThe US is still firmly in the lead regarding its proportion of AI for Drug Discovery companies.Interestingly,Asia and the Middle East continue to expand usage of AI technologies in the Pharmaceutical Industry.The ratio of companies that use AI for Drug Development in the UK and European countries is decreasing compared to the Asian market.The Asia-Pacific region continues to aggressively increase the number of AI for Drug Discovery Companies,particularly in China,and this tendency will probably maintain.9Deep Pharma IntelligenceUS57.74nada4.83%UK8.40%China4.20%Asia5.46%Australia0.42%EU25.00%Middle East7.3520 Investors:Regional ProportionThe United States continues to lead the rest of the world in terms of artificial intelligence for companies and funds that invest in Drug Discovery.This is reasonable,given that more than a half of the worlds AI for Drug Discovery companies have their headquarters in USA.Comparing with previous periods of 2021,we can observe significant growth of the number of investors in China,as well as in US as Europe.Thus,together with UK these regions are leaders by the number of investors in AI in Drug Discovery companies.10Deep Pharma IntelligenceCanada4.06%US52.78%UK6.00%EU18.71%China5.65%Asia&Middle East6.89%Australia1.06P Leading Investors:Regional ProportionThe United States continues to lead the rest of the world in terms of artificial intelligence for companies and funds that invest in Drug Discovery.This is reasonable,given that more than a half of the worlds AI for Drug Discovery companies have their headquarters in USA.During 2021 we can observe significant growth of the number of investors in Asia,mainly in China,Hong Kong and Singapore.The USA,the UK,and EU remain to be leaders by the number of investors in AI in Pharma companies.11Deep Pharma IntelligenceCanada1.85%UK5.56%EU5.56%US79.63%China3.70%Singapore3.70%Sources:Investment Digest AI in PharmaPharmas“AlphaGo Moment”12Deep Pharma Intelligence1990201220152018-192020-21Practical ValueFundamental breakthroughs in AI theory (DL,NLP,etc)ImageNet and the rise of practical DLGANs and other advanced NN structures“AlphaGo Moment”in pharma:practical validation in drug design,biotech R&DMature technology.Strategic competition for AI startups,a rising wave of investments/M&A deals2022-23Widespread adoption of AI in pharma (VR,digital twin,automated data analysis etc.),prioritisation of AI in R&D Experimental ResultComputational PredictionNotable Breakthroughs in AI for Pharma13Deep Pharma IntelligenceDeep Genomics AI-driven platform predicted novel target and oligonucleotide candidate for Wilson disease in under 18 months.Insilico Medicine applied generative adversarial network-based system GENTRL for rapid identification of potent DDR1 Kinase inhibitors within 21 days.DeepMinds AlfaFold learns to predict proteins 3D shape from its amino-acid sequence,a 50 year-old grand challenge in biology.The University of Washington has developed a deep learning model,“RoseTTAFold”,that calculates protein structure on a single gaming computer within 10 minutes.Model201920202021ExperimentTechnological Advancements Defining the Market14Deep Pharma IntelligenceInsilico Medicine achieved industry-first fully AI-based Preclinical Candidate.Initial hypothesis was build via DNN analysis of omics and clinical datasets of patients.After that company used its AI PandaOmics engine for target discovery,analyzing all relevant data,including patents and research publications with NLP algorithms.In the next step Insilico has applied its generative chemistry module(Chemistry42)in order to design a library of small molecules that bind to the novel intracellular target revealed by PandaOmics.The series of novel small molecules generated by Chemistry42 showed promising on target inhibition.One particular hit ISM001 demonstrated activity with nanomolar(nM)IC50 values.When optimizing ISM001,Insilico managed to achieve increased solubility,good ADME properties,and no sign of CYP inhibition with retained nanomolar potency.Interestingly,the optimized compounds also showed nanomolar potency against nine other targets related to fibrosis.The efficacy and a good safety of the molecule led to its nomination as a pre-clinical drug candidate in December 2020 for IND-enabling studies.The phase I clinical trial for the novel drug candidate is planned for December 2021.1 week2 months4 months11 monthsPhase 1Phase 2Phase 3Submission to launchDisease Hypothesis&Target IdentificationTarget-to-hitHit-to-leadLead optimization and Candidate ValidationPreclinical candidate Selected(PCC)Up to Decades1 year1.5 years2 yearsPhase 1Phase 2Phase 3Submission to launchInsilico$50kN/A$200k$94M$400k$166M$200k$414MTraditional ApproachSource:Insilico Medicine 2021Executive SummaryDEEP PHARMA INTELLIGENCEThis 135-page“Artificial Intelligence for Drug Discovery Landscape Overview,Q3 2022”report marks the installment in a series of reports on the topic of the Artificial Intelligence(AI)application in pharmaceutical research industry that DPI have been producing since 2017.The main aim of this series of reports is to provide a comprehensive overview of the industry landscape in what pertains adoption of AI in drug discovery,clinical research and other aspects of pharmaceutical R&D.This overview highlights trends and insights in a form of informative mind maps and infographics as well as benchmarks the performance of key players that form the space and relations within the industry.This is an overview analysis to help the reader understand what is happening in the industry nowadays and possibly give an idea of what is coming next.Alongside investment and business trends,the report also provides technical insights into some of the latest achievements in the AI application and research.Report at a Glance16Deep Pharma IntelligenceLatest AchievementsNotable Case StudiesAI-Pharma CollaborationsBusiness TrendsTechnical InsightsCROsTech Companies600 AI BioTech Companies1200 InvestorsPharma CompaniesPharma Efficiency:Challenges17Deep Pharma Intelligence10 years $2.6 bln=1 new drugIt takes on average over 10 years to bring a new drug to market.As of 2014,according to Tufts Center for the Study of Drug Development(CSDD),the cost of developing a new prescription drug that gains market approval is approximately$2.6 billion.This is a 145%increase,correcting for inflation,compared to the same report made in 2003.The pharmaceutical industry is in a terminal decline,and the returns on new drugs that do get to market do not justify the massive investments that Pharma currently puts into R&D anymore.The solution to this problem comes from three key strategies:evolution of business models towards more collaboration and pipeline diversification early implementation of AI as a universal shift towards data-centric drug discovery discovery of new therapeutic modalities(biologics,therapies,etc.)0-Effect on bodyI-Safety in humansII-Effectiveness at treating diseasesIII-Larger scale safety and effectivenessIV-Long term safety1 approved drug5,000,000 compounds500 compounds5FDAPre-clinical developmentClinical developmentRegulatory approvalResultDrug discoverySource:Conflict of Interest in Medical Research,Education,and Practice,Computer-aided Drug Design18Deep Pharma IntelligenceScreeningModellingDe novo designTodays task for the pharma industry is to create a cheap and effective solution for drug development,companies apply various computational methods to reach that goal.Computer-aided drug design(CADD)is a modern computational technique used in the drug discovery process to identify and develop a potential lead.CADD includes computational chemistry,molecular modeling,molecular design and rational drug design.Sources:Advantages of Structure-Based Drug Design Approaches in Neurological Disorders.CADDDiscoveryMolecule SelectionOptimizationDatabasesValidationTarget Identification Homology Modelling Molecular Modelling Structure based Shape based Pharmacophore based Druggability pocket Compound identification QSAR Lead Optimization Docking and scoring Library design Affinity evaluation ADME estimation Small molecule databases 3D structure database Molecular fragment databaseComputer-aided Drug Design19Deep Pharma IntelligenceDatabasesSmall molecule databases3D structure databasesMolecular fragment databasesStructure-based virtual screeningBinding energy analysisScoringDockingChemical intuitionMolecular dynamic simulationAnalyze the interaction of target structure and lead candidateModellingHomology ModellingMolecular ModellingFunctional GenomicsTarget protein identificationBinding site predictionSources:Advantages of Structure-Based Drug Design Approaches in Neurological Disorders.Modern computational structure-based drug design has established novel platforms that mostly have a similar structure for testing drug candidates.The usage of AI can simplify and facilitate the drug design from filtering datasets for appropriate compounds to advanced lead modification and in silico testings.Big Pharmas AI-focused partnerships till Q3 2022In this report we have profiled 600 actively developing AI-driven biotech companies.A steady growth in the AI for Drug Discovery sector can be observed in terms of substantially increased amount of investment capital pouring into the AI-driven biotech companies($2.28B in HY 2020 against$2.93B in HY 2021),the increasing number of research partnerships between leading pharma organizations and AI-biotechs,and AI-technology vendors,a continuing pipeline of industry developments,research breakthroughs,and proof of concept studies,as well as exploding attention of leading media and consulting companies to the topic of AI in Pharma and healthcare.Some of the leading pharma executives increasingly see AI as not only a tool for lead identification,but also a more general tool to boost biology research and identify new biological targets and develop novel disease models.The main focus of AI research for today is still on small molecules as a therapeutic modality.20Deep Pharma Intelligence31 Deals28-38 Deals27-32 Deals23-27 DealsApplication of AI for Advanced R&D to Address Pharma Efficiency Challenges21Deep Pharma IntelligenceClinical TrialsAI for Advanced R&DTarget Discovery and Early Drug Discovery Aggregation and Synthesis of InformationRepurposing of Existing DrugsDesign and Processing of Preclinical ExperimentsAccelerated development of new drugs and targets identification Identify novel drug candidates Analyze data from patient samples Predict pharmacological properties Simplify protein designTime-and resources-efficient information managementGenerate insights from thousands of unrelated data sourcesImprove decision-makingEliminate blind spots in researchSearching for new applications of existing drugs at a high scale Rapidly identify new indications Match existing drugs with rare diseases Testing 1000 of compounds in 100 of cellular disease models in parallelOptimization of experiments and data processing Reduce time and cost of planning Decode open-and closed-access data Automate selection,manipulation,and analysis of cells Automate sample analysis with a robotic cloud laboratoryTargeted towards personalized approach and optimal data handling Optimize clinical trial study design Patient-representative computer models Define best personalized treatment Analyze medical records Improve pathology analysisBusiness ActivityInsitro has raised$400M for machine learning-powered drug discovery efforts.The financing was led by the Canada Pension Plan Investment Board with additional backing from Andreessen Horowitz,Casdin CapitalValo Health announced the final closing of its Series B at$300M,including a$110 million investment from Koch Disruptive Technologies(KDT).This brings the overall funding of Valo to over$450M to accelerate the creation of life-changing drugsAmgen Mila partnership that permits Amgen to expand its knowledge of AI and deep learning by interacting and engaging with experts in Milas unique ecosystemExscientia sealed a$5.2B deal(biggest deal of A.I.)to expand an ongoing collaboration agreement with Sanofi to include 15 new molecules.Anumana,Janssen and Mayo Clinic have developed ECG-based Pulmonary Hypertension(PH)Early Detection Algorithm which will help doctors identify pulmonary hypertension early,a condition that is progressive and life-threatening.Microsoft and Novo Nordisk signed a contract to expedite the companys drug discovery process.22Deep Pharma IntelligenceThe business activity has been increasing in the pharmaceutical AI space over Q1 2022-Q3 2022,judging by an increased number of transactions and partnership announcements in this period.The most significant deals and collaborations in include:Dynamics of Investments in AI in Drug DevelopmentThere has been a substantial increase in the amount of capital invested in AI-driven pharma companies since 2014.During the last seven years,the annual amount of investments in 600 companies has increased by almost 52 times(to$115.84B in total as of October 2022).In 2021,the flow of investments increased by 143%compared to the previous year.The estimated amount of investments in the AI in Pharma sub-sector of the Longevity industry has increased in 2.5 times in 2021 compared to 2020 which identifies strong investors(foremost VCs)interest in this field regardless of risks.23InvestTech Advanced SolutionsDeep Pharma IntelligenceTop 10 AI in Pharma Companies by Total Investments in Q2-Q3 202224InvestTech Advanced SolutionsDeep Pharma IntelligenceThe chart shows the top 10 AI-driven drug discovery companies sorted by the total funding raised by the end of Q3 2022.XtalPi,an artificial intelligence-powered drug R&D company,is now at the top of the list.Having completed the business combination with Excelra,XtalPi has the total funding raised to$791M.Insitro,american company utilizing ML drug discovery,could finance$743M in capital market.Tempus,Insitro and ThoughtSpot are new companies due to late-stage mega-rounds during the 2021.Major Observations for Q2-Q3 2022:Key Business TakeawaysThe segment of pharmaceutical AI continues consolidation with the increasing number of later stage mega-rounds,including XtalPi,Neuromora Therapeutics and Insitro(both$400M),Medable and Biofourmis(both$304M),Insilico Medicine($255M),and DNAnexus and Genuity Science(both$200M).The AI start-up pack is clear leaders with significant resources,financial leverage,technical edge,and laggards with fewer finances,technology,and scientific assets.Besides,there is one company that received IPO status recently:Benevolent AI.25Deep Pharma IntelligenceThe pharmaceutical AI business is“heating up”,becoming a profitable area for expert biotech investors as well as investor groups looking to diversify their portfolios with high-risk/high-reward firms.A growing number of proof-of-concept breakthroughs confirm that AI technology has matured enough to provide tangible value to pharma and contract research organizations(CROs).Due to quickly growing proof of AI tech feasibility and innovation potential,big pharma and contract research organizations are actively competing for AI collaborations.Amgen and Generate Biomedicines will team together to find and develop protein therapies for five clinical targets using a variety of treatment methods and therapeutic regions.Cyclica has announced 10 new academic partnerships.With its new agreements,Cyclica hopes to equip academia academics with AI-enhanced drug development platforms and hasten the process.Major Observations for Q2-Q3 2022:Key Business TakeawaysThe global COVID-19 pandemic prolongs the rise of the overall biotech and drug discovery sectors.During 2021 we have observed over 100 medium and large funding rounds for biotech and drug design companies,especially those focused on antiviral therapies and vaccines.26Deep Pharma IntelligenceIn Q2-3 2022,1 company that use AI for DD reached IPO status.London-based Benevolent AI closed its IPO in April and raised$292M.The vast majority of companies started gaining IPO status after 2018,marked by a growth of 136.0%during the last four years and we expect this trend growth to continue.When some of the companies complete IPOs in the nearest future,it will attract a significant number of non-biotech investors to enter the Life Sciences sector.The prospects of this trend are already vivid:big tech companies enter partnerships with both innovative start-ups and pharma companies to consolidate resources,mainly in personalized medicine,cell and gene therapy,and molecule prediction software.Some of these companies even open subsidiaries harvesting AI in Drug Design(like Isomorphic Labs from Google).The growing industry traction,reflected in the increasing number of R&D partnerships between big pharma and CROs with AI startups,is a sign that the market is maturing for rapid increase in M&A activity in the nearest future.Because of the crisis AI-in-Drug Development publicly traded companies fell to$85,7B of cumulative capitalization as of October 3rd,2022.Key Technology Takeaways1.AI is regarded by some top executives at big pharma(GSK and others)as a tool to uncover not only new molecules,but also new targets.Ability of deep neural networks to build ontologies from multimodal data(e.g.“omics”data)is believed to be among the most disruptive areas for AI in drug discovery,alongside with data mining from unstructured data,like text(using natural language processing,NLP).2.There is a considerable trend for“AI democratization”where various machine learning/deep learning technologies become available in pre-trained,pre-configured“of-the-shelf”formats,or in relatively ready-to-use formats via cloud-based models,frameworks,and drag-and-drop AI-pipeline building platforms(for example,KNIME).This is among key factors in the acceleration of AI adoption by the pharmaceutical organizations where a non-AI experts can potentially use fairly advanced data analytics tools for their research.3.Proof-of-concept projects keep yielding successful results in research studies,and in the commercial partnerships alike.For example,companies like Recursion Pharmaceuticals,Insilico Medicine,Deep Genomics,and Exscientia achieved important research milestones using their AI-based drug design platforms.27Deep Pharma IntelligenceAI democra-tizationsAI platforms yield successful resultsAI on different steps in DDAI is used not only for drug design,but also target identification.Many AI-designed drugs showed successful results in research studies and even clinical trials.Ready-to-use AI platforms for DD became available and can be used by non-AI experts.AI in Biotech ChallengesLack of Quality DataObstacles That Still Remain28Deep Pharma Intelligence1.Global shortage of AI talent continues to be a serious challenge for the biopharma industry,repeating the trend from our previous reports.While big pharmaceutical companies invest substantial capital in recruitment of AI specialists,still the majority of them are acquired by large tech corporations(Google,Amazon,Alibaba,Tencent,Baidu etc.)However,a growing wave of specialized university programs and courses,geared towards data science and AI application,is projected to address this issue to certain extent in the coming years.2.Lack of available quality data is still a challenge for the unleashing full potential of deep learning technologies.Numerous variations of deep learning(DL)are believed to be the most lucrative area of AI for applications such as drug discovery and clinical research.The key challenge is that DL algorithms are“data-greedy”,while big data in biotech is not always well-versed for modeling,or is inaccessible due to privacy reasons.3.Ethical,legal,and regulatory issues for AI adoption in the pharmaceutical sciences.This set of challenges is related to the previous point,but also includes other questions AI explainability,patentability of AI-generated results,non-optimal regulations in various countries,slowing down the progress and adoption of AI technologies in general,and in the pharmaceutical industry in particular.Lack of Specialists Ethical,Legal and Regulatory IssuesAI in the Global Context29US is a main player in AI industry In the beginning of AI implementation,US was a pioneer and then the main player with the greatest number of companies using AI to force R&D,research centres and institutes,and investments.China engages in extensive investment activityIn particular,it has promised to invest$5B in AI.Tianjin,one of the biggest municipalities,is going to invest$16B in its local AI industry,and the Beijing authorities will build$2.12B AI development project.China plans to become the world AI leader by 2030According to the AI Strategic Plan released in July 2017.The analysis of the the Asia-Pacific region has shown that the main forcers of AI implementation include Saama Technologies,Inc.,a leading clinical data analytics company.Europe has traditionally been a strong breeding ground for biopharma activity The UK and EU activity in the pharmaceutical AI race is mainly boosted by Novartis.UK-based BenevolentAI and AstraZeneca collaborate with novel AI-generated chronic kidney disease target.DEEP PHARMA INTELLIGENCEBusiness Activity:Overview Round A Round B Round C and others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2M$10MApollo Hospitals EnterpriseSchrodinger$562M$4.9MApollo Hospitals EnterpriseBioforumis$300M$143MApollo Hospitals EnterpriseRelay Therapeutics$350M$520MApollo Hospitals EnterpriseBerkeley Lights$20M$252MApollo Hospitals EnterpriseBenevolentAI$90M$202MApollo Hospitals EnterpriseExscientia$100M$375MApollo Hospitals EnterpriseInsitro$400M$343MApollo Hospitals EnterpriseXtalPi$400M$386MApollo Hospitals EnterpriseApollo Hospitals EnterpriseAtomwiseValo Health$2.3M$174M$110M$300MFunding amount prior to the last dealFunding amount by the last deal Round A Round B Round C and Others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2MFunding amount prior to the last dealFunding amount by the last deal$10MApollo Hospitals EnterprisePatSnap$52M$300MApollo Hospitals EnterpriseInsilico Medicine$60M$306MApollo Hospitals EnterpriseAetion$110M$94MiCarbonX$20M$252MApollo Hospitals EnterpriseNimbus Therapeutics$105M$197MApollo Hospitals EnterpriseStandigm$10M$61MApollo Hospitals EnterpriseCellarity$123M$50MApollo Hospitals EnterpriseBIOAGE LABS$90M$34MApollo Hospitals EnterpriseIndigenePathAI$165M$90M$200M$155M$45M Round A Round B Round C and others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2M$10MApollo Hospitals EnterpriseNeuron23$114M$100MApollo Hospitals EnterpriseStoneWise$100M$10MApollo Hospitals EnterpriseStrateos$56M$46MApollo Hospitals EnterpriseGENFIT$45$48MApollo Hospitals EnterpriseSangamo Therapeutics$145M$377MApollo Hospitals EnterpriseTurbine AI$7M$88MApollo Hospitals EnterpriseDatavant$40MApollo Hospitals EnterpriseRecursion Pharmaceuticals$239M$225MApollo Hospitals EnterpriseApollo Hospitals EnterpriseDNAnexusNference$200M$2734M$61M$119M$40.5MFunding amount prior to the last dealFunding amount by the last deal Round A Round B Round C and others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2M$10MApollo Hospitals EnterpriseRoivant Sciences$1 900M$200MApollo Hospitals EnterpriseTempus$200M$850MApollo Hospitals EnterpriseHuman Longevity$30M$300MApollo Hospitals EnterpriseSynergy Pharmaceuticals$300$107MApollo Hospitals EnterpriseGritstone Oncology$55M$341MApollo Hospitals EnterpriseFlatiron Health$11.9M$313MApollo Hospitals EnterpriseErasca$36MApollo Hospitals EnterpriseSOPHiA GENETICS$110M$140MApollo Hospitals EnterpriseApollo Hospitals EnterpriseITeos TherapeuticsNference$125M$125M$11.7M$290M$264MFunding amount prior to the last dealFunding amount by the last deal Round A Round B Round C and others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2M$10MApollo Hospitals EnterpriseIDEAYA Biosciences$140M$86MApollo Hospitals EnterpriseNeon Therapeutics$200M$125MApollo Hospitals EnterpriseNeumora Therapeutics$500MApollo Hospitals EnterpriseProscia$36.6M$34MApollo Hospitals EnterpriseMedable$304M$203Apollo Hospitals EnterpriseFoundation Medicine$13.5M$83MApollo Hospitals EnterpriseOwkin$20MApollo Hospitals EnterpriseAlector$133M$62MApollo Hospitals EnterpriseApollo Hospitals EnterpriseAi TherapeuticsArrakis Therapeutics$58M$40$75M$38M$254MFunding amount prior to the last dealFunding amount by the last deal$36M50 Leading Investors in Pharmaceutical AIDEEP PHARMA INTELLIGENCE37Deep Pharma IntelligenceAlexandria Venture InvestmentsSOSVNational Science FoundationRA Capital ManagementCasdin Capital1Creative Destruction Lab(CDL)234GV5Y Combinator6Perceptive Advisors7Sequoia Capital China8910Merck Global HealthAlumni VenturesForesite Capital1112Khosla Ventures13148VC15DCVC Bio16National Institute of Health17EASME-EU Executive Agency18MassChallenge19T.Rowe Price20DeerfieldF-Prime CapitalSurveyor CapitalSoftBank Vision Fund21Invus222324Redmile Group25DCVC Bio26Founders Fund27IndieBio28Fidelity Management2930Temasek HoldingCormorant Asset ManagementNorthpond Ventures31325Y Capital3334 Obvious Ventures35Andreessen Horowitz36Section 3237Lux Capital38AME Cloud Ventures39Eight Roads Ventures40BlackRockForesite CapitalBiotechnology Value FundLifeforce Capital41Felicis Ventures4344Janus Henderson Investors45Tencent46ARCH Venture Partners47Novo Holdings48Flagship Pioneering49504250 Leading Investors in AI for Drug Discovery SectorCreative Destruction Lab(CDL)Toronto,CandaTop-50 AI in Pharma Investors38Deep Pharma IntelligenceObvious VenturesSan Francisco,California,USLifeforce CapitalSan Francisco,California,USSan FranciscoAlexandria VentureSan Francisco,California,USForesite CapitalSan Francisco,California,USFounders FundSan Francisco,California,US8VCSan Francisco,California,USDCVC BioSan Francisco,California,USDCVCSan Francisco,California,USNew YorkCasdin CapitalNew York,New York,USInvusNew York,New York,USPerceptive AdvisorsNew York,New York,USBristol-Myers SquibbNew York,New York,USOrbiMedNew York,New York,USMountain ViewY CombinatorMountain View,California,USGVMountain View,California,USPalo AltoAME CLoud VenturesPalo Alto,California,USLili VenturesIndianapolis,Indiana,USSOSVPrinceton,New Jersey,USNational Institute of HealthMaryland,UST.Rowe PriceBaltimore,Maryland,USNational Science FoundationAlexandria,Virginia,USAltitude Life Science VenturesWashington,USOther StatesManhattan BeachB Capital GroupManhattan Beach,California,USMenlo ParkAndreessen HorowitzMenlo Park,California,USFelicis VenturesMenlo Park,California,USKhosla VenturesMenlo Park,California,USMassachusettsThird Rock VenturesBoston,Massachusetts,USRA Capital ManagementCambridge,Massachusetts,USF-Prime CapitalCambridge,Massachusetts,USAlexandria Venture InvestmentsPasadena,California,USIllinoisDeerfield Capital Rosamond Ridge,Illinois,USARCH Venture PartnersChicago,Illinois,USEDBISingapore,Central RegionNovo HoldingsHellerup,Hovedstaden,DenmarkCounterpoint GlobalLondon,England,The UKSoftBank Vision FundLondon,England,The UKBaillie GiffordEdinburgh,Edinburgh,The UKJanus Henderson InvestorsLondon,England,The UKBeijingZhenFundBeijing,ChinaPing An BankShenzhen,ChinaSequoia Capital ChinaBeijing,ChinaShanghaiLilly Asia VenturesShanghai,ChinaGT Healthcare Capital PartnersCentral,Hong Kong Island,Hong Kong5Y CapitalShanghai,ChinaTencentShenzhen,ChinaTemasekSingapore,Central RegionCormorant Asset ManagementBoston,Massachusetts,USHBM Healthcare Investments AGZug,SwitzerlandRocheBasel,SwitzerlandMarylandNorthpond VenturesMaryland,USMassChallengeBoston,Massachusetts,USTop-50 Investors in AI CompaniesINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATIONINVESTED INCasdin Capital19USAAbsci,Alector,Arzeda,Beacon Biosignals,Celsius Therapeutics,Clover Therapeutics,Exscientia,Gritstone Oncology,Fabric Genomics,Flatiron Health,Foundation Medicine,Lunit,Insitro,Paige,Recursion Pharmaceuticals,Relay Therapeutics,Sema4,ShouTi,SomaLogic,Treeline BiosciencesCreative Destruction Lab(CDL)15CanadaBiotx.ai,DeepCure,DeepLife,Entropica Labs,Epistemic AI,Juvena Therapeutics,Kyndi,Kuano,Menten AI,NetraMark,OrganoTherapeutics,ProteinQure,Winterlight Labs,Valence Discovery SOSV14USAA2A Pharmaceuticals,Gatehouse Bio,Guided Clarity,Mendel.ai,Stelvio Therapeutics,Strados,SynthaceNational Science Foundation14USAbioSyntagma,ADM Diagnostic,Bioz,Cloud Pharmaceutical,Data2Doscovery,Strados Labs,VeriSIM Life,TeselaGen,GV13USADNAnexus,Flatiron Health,Foundation Medicine,IDEAYA Bioscience,insitro,Owkin,Schrdinger,Relay Therapeutics,Ultromics,Celsius Therapeutics,Alector,Y Combinator12USAHistoWiz,iLab Service,Menten AI,Reverie Labs,Segmed,Arpeggio Bio,Athelas,Atomwise,CloudMedx,Coral GenomicsPerceptive Advisors11USAAbsci,Alector,Black Diamond Therapeutics,Champions Oncology,DNAnexus,Icosavax,IDEAYA Biosciences,Neuron23,Saama,Sema4,Soma Logic,Relay TherapeuticsAlexandria Venture Investments11USAArrakis Therapeutics,Celsius Therapeutics,Deep Genomics,GNS Healthcare,Gritstone Oncology,IDEAYA Biosciences,Immunai,Insitro,Fountain Therapeutics,LEXEO Therapeutics,Neuromora Therapeutics,Veralox TherapeuticsSequoia Capital China10 ChinaMETiS Therapeutics,PatSnap,Transcenta,XtalPi,Adagene,Athelas,Biofourmis,Deep Intelligent Pharma,HiFiBiO,Genuity BioRA Capital Management9USANimbus Therapeutics,Wave Life Sciences,Bristol Myers Squibb,Xbiome,Everest Medicines,Freenome,Frontier Medicines,Icosavax39Deep Pharma IntelligenceTop-50 Investors in AI Companies40Deep Pharma IntelligenceINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATION INVESTED INMerck Global Health9 USAOpGen,PathAI,PreciseDx,Strata Oncology,Verge Genomics,Absci,Antidote.me Alumni Ventures9USAEmerald Cloud Lab,Notable Labs,Olaris,Scipher Medicine,Strateos,Unlearn.AI,Veralox Therapeutics,Verge Genomics Khosla Ventures8USAArpeggio Bio,Atomwise,BIOAGE LABS,Fountain Therapeutics,Deep Genomics,Menten AI,Ochre Bio,Scipher Medicine,ThoughtSpot Foresite Capital8USAAetion,DNAnexus,Insitro,Relay Therapeutics,Wave Life Sciences 8VC8 USABigHat Biosciences,Coral Genomics,Immunai,Model Medicine,Notable,ProteinQure,Unlearn.AI DCVC Bio8 USAEmpirico,Frontier Medicines,Totus Medicines,Unlearn.AI,X-37 National Institute of Health8USAImaginostics,PostEra,Sangamo Therapeutics,SEngine Precision Medicine,Simulations Plus,Virvio,bioSyntagma,Coral GenomicsEASME-EU Executive Agency for SMEs8USAQuibim,Acellera,CellPly,Cytox,Genome Biologics,Genialis MassChallenge8USAScailyte,Simply Speak,Strados Labs,Vyasa Analytics,ChemAlive sA,Agamon,OrganoTherapeutics T.Rowe Price7USAArbor Biotechnologies,Generate Biomedicines,Genesis Therapeutics,Insitro,Sema4,SomaLogic,TempusTop-50 Investors in AI Companies41Deep Pharma IntelligenceINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATION INVESTED IN SoftBank Vision Fund7UKBiofourmis,Datavant,Deep Genomics,Exscientia,Insitro,PatSnap,Relay Therapeutics,Roivant Sciences,XtalPi Invus7USAValo Health,Black Diamond Therapeutics,Engine Biosciences,Erasca,ITeos Therapeutics,Neumora Therapeutics,Schrdinger Deerfield7USASema4,Strata Oncology,Alector,ConcertoCare,Foundation Medicine,Frontier Medicines,Insilico Medicine,Schrdinger F-Prime Capital7USABenchSci,Neumora Therapeutics,Notable,Owkin,Peptone,Adagene Redmile Group7USAFoundation Medicine,Gritstone Oncology,Neuron23,Wave Life Sciences,Absci DCVC Bio7USAEmpirico,Frontier Medicines,Totus Medicines,Unlearn.AI,X-37 Founders Fund7USAAbCellera Biologics,Datavant,Emerald Cloud Lab,Notable Labs,Roivant Sciences,DeepMind IndieBio7USAGatehouse Bio,Guided Clarity,Stelvio Therapeutics,A2A Pharmaceuticals Fidelity Management6USARoivant Sciences,Sema4,Wave Life Sciences,Absci,Deep Genomics,Generate Biomedicines,Surveyor Capital6USAShouTi,Arbor Biotechnologies,Icosavax,Neumora Therapeutics,Neuron23,Nimbus TherapeuticsTop-50 Investors in AI Companies42Deep Pharma IntelligenceINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATION INVESTED INTemasek Holding6SingaporeTranscenta,BenevolentAI,Genuity Science,Glympse Bio,InsitroCormorant Asset Management6 SwitzerlandStrata Oncology,Wave Life Sciences,Biomea Fusion,Erasca,Icosavax 5Y Capital6ChinaXbiome,XtalPi,AliveX Biotech,Galixir,METiS TherapeuticsNorthpond Ventures6USADeep Lens,DNAnexus,Outcomes4Me,Scipher Medicine,Totus Medicines Obvious Ventures6 USALabGenius,Medable,Recursion Pharmaceuticals,ConcertoCare,Inato Andreessen Horowitz6USAAria Pharmaceuticals,Asimov,BigHat Biosciences,BIOAGE LABS,Freenome Section 326USACharacter Biosciences,Glympse Bio,Nucleai,Verge Genomics,Alector Lux Capital6 USAAlife,Auransa,LabGenius,Recursion Pharmaceuticals,Strateos AME Cloud Ventures6 USAAsimov,Atomwise,Auransa,BigHat Biosciences,BIOAGE LABS Eight Roads Ventures6UKOwkin,ShouTi,WuXi AppTec,AdageneTop-50 Investors in AI Companies43Deep Pharma IntelligenceINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATION INVESTED IN Lifeforce Capital6USAPostEra,TARA Biosystems,Verge Genomics,Character Bioscience Felicis Ventures6USAJuvena Therapeutics,LabGenius,ProteinQure,Spring Discovery BlackRock5USAVerge Genomics,Cellarity,Exscientia,Insitro,Sema4 Foresite Capital5USAWave Life Sciences,Aetion,DNAnexus,Insitro,Relay Therapeutics Janus Henderson Investors5USAEverest Medicines,LEXEO Therapeutics,ShouTi,SomaLogicTencent5ChinaAtomwise,Brainomix,iCarbonX,PatSnap,XtalPi ARCH Venture Partners5 USAArbor Biotechnologies,Erasca,Generate Biomedicines,Glympse Bio Novo Holdings5DenmarkKebotix,Tempus,Evotec,Exscientia,Flagship Pioneering5USAValo Health,Cellarity,Generate Biomedicines,Biotechnology Value Fund5USAEvotec,Gritstone Oncology,IDEAYA Biosciences,Nimbus TherapeuticsBig Pharmas Focus on AIDEEP PHARMA INTELLIGENCEAI and Pharma Collaborations in Q2 2022-Q3 202245Feb 2022Mar 2022Amgen collaborated with Generate Biomedicines to create protein therapeutics for five clinical targets.Amgen will pay potentially up to$1.9 billion in this collaboration for a novel AI driven platform Bayer,Aalto and HUS expanded collaboration to apply artificial intelligence to support clinical drug trialsApr 2022Jun 2022Aqemia and Sanofi will work together on a number of initiatives in cancer,a major therapeutic area for Sanofi,to design and find new medicines.Takeda and Evozyne will create novel gene therapies for up to four rare disease targets.The deal worth up to$400 millionAug 2022Sep 2022The AI partnership between Bayer and Exscientia,which saw the two parties search for cardiovascular and cancer targets came to an end.Sanofi focuses on using Atomwises AtomNet platform to conduct small molecule research on up to five therapeutic targets.Jan 2022May 2022Elix announced a research partnership with Shionogi on the validating retrosynthetic analysis utilizing data from Shionogi.AstraZeneca obtains a second pulmonary fibrosis target with a partnership with BenevolentAISelected Pharma AI Deals AI Companies Pharma CorporationsAI Companies 4646Deep Pharma IntelligencePharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.Selected Pharma AI Deals 47Deep Pharma IntelligenceAI Companies Pharma CorporationsAI Companies Pharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.A Growing Number of Collaborations Involving AI for Drug DiscoverySummarizing industry observations over the last five years,we can observe a fundamental shift in perception of top executives at leading pharmaceutical organizations about the need of advanced AI technologies.Since 2015,there has been an obvious shift in the perception from skepticism and cuasious interest,all the way to a realization of a strategic role AI has to play in the emerging“data-centric”model of innovation.This change in perception was underpinned by a number of factors:a wave of proof-of-concept studies and research breakthroughs in a wide range of AI application use cases a number of commercial successes and successfully reached milestones,involving AI as a central element of research substantial advances in democratizing AI technology,where machine learning and deep learning algorithms become available at scale to non-AI experts decent increase in the overall understanding of AI“mechanics”,due to increasing efforts in the education and professional development with a focus on AI-driven tools and approachesPharmaceutical companies of all sizes start competing for AI-expertise,talent,and partnerships.In this report we summarize some of the most high-profile such collaborations,involving top-20 pharma giants.Even though,we can see a clear uprising trend in the number of collaborations,focused on AI-drug design,and other aspects of data mining and analytics.Deep Pharma Intelligence48The rising interest of leading pharma and contract research organizations towards AI-driven biotech startups is a major driver for the area to become more attractive for investors,since the industry is becoming well-suited for successful exit strategies in future.Increasing number of partnerships between Pharma and AI Companies over the last 6 yearsCorporation and AI-companies Participating in the Pharma AI DealsPharma PartnersAI and Biotech Partners49Deep Pharma IntelligenceTech PartnersThe leading Pharma players by the amount of major industry partnerships are AstraZeneca and Merck.These companies demonstrate increasing commitment to probing the grounds in the AI space by investing into internal programs,as well as partnering with external AI vendors to pilot programs in drug discovery and other research areas.The most common type of deals are true partnerships and saving the costs deals.The leading big pharma brands are increasingly open to partnerships with AI startups and corporations to getcompetitive edge,and mitigate theproblem of declining R&D efficiency.50Deep Pharma IntelligenceLeading Pharma Corporations by the Number of Pharma AI Deals in Q3 202251Deep Pharma IntelligenceThe leading AI players by the amount of major industry partnerships are Insilico Medicine,IKTOS and Atomwise.The biggest number of AI in Drug Discovery deals was conducted by Insilico Medicine.The company is an end-to-end,AI-driven pharma-technology company that accelerates drug development by proprietary platform across biology,chemistry and clinical development.All of the deals concluded with this company were categorized as the ones aiming at saving costs and increasing operational efficiency due to thecharacter of the services provided.Top-10 AI and Tech Partners by Number of Major Pharma AI Deals in 2021-Q3 2022DEEP PHARMA INTELLIGENCEAI in Pharma Publicly Traded CompaniesAI in Pharma Publicly Traded CompaniesDespite the crisis and high volatility,AI-in-Pharma publicly traded companies present growth reaching$85,7B of cumulative capitalization as of October 3,2022.About 50 AI in Drug Development companies were taken for this analysis,one of them Benelovent AI has closed its IPO in Q3 2022.The largest companies by market capitalization are Evotec,AbCellera and Relay Therapeutics.The smallest market capitalization are in Pharnext SA,Deepmatter Group and OpGen Inc.Its essential to measure the performance of publicly traded AI in Drug Development companies via comparison with major market benchmarks such as IBB,NBI and S&P 500.Because of the crisis,the cumulative market capitalization dynamics of AI in Pharma corporations are losing to YTD NASDAQ Biotechnology Index(NBI),iShares Biotechnology ETF(IBB),and S&P 500 gained solid.53Deep Pharma IntelligenceCumulative Capitalization of Publicly Traded AI-in-Drug Development Companies,Q2-Q3 2022,$BillionMarket Capitalization Growth During Q2-Q3 2022$110B$100B$900B$800B$700B1-June-20221-July-20221-August-20221-September-2022Top-10 AI-Driven Publicly Traded Pharma Companies by Market Capitalization in 202254Deep Pharma IntelligenceThe chart presents the Top-10 AI-driven drug discovery public companies arranged by market capitalization as of end of September 2022.AbCellera,British Columbia-based biotechnology firm that researches and develops human antibodies holds the first place with$2.8B of market capitalization.$3B$2B$1B$2.8B$2.7B$2.3B$2.3B$1.8B$1.8B$1B$1B$0.9B$0.8BAI in Pharma IPOs in Q2-Q3 202255Deep Pharma IntelligenceIn Q2 2022,BeneloventAI has successfully closed IPO.The IPO took place in the UK.The company has beta smaller than 1(although positive),which means that AI in pharma stock prices move following the general market,yet the degree of such“movements”is lower.Major adverse market events in 2020-2022 did not significantly affect AI in pharma sector.The industrys features remain to play a designative role in the overall market volatility.Benevolents PlatformTM is a powerful computational R&D platform.Scientists may query the data and disease networks inside the graph using Benevolents range of exploratory and predictive AI tools.They can also ask biological queries,generate fresh insights,and prioritize ideas.In order to detect dysregulated pathways and processes and visualize the major distinctions between health and sickness,this enables researchers to target the most effective therapeutic approaches.The graph on the left depicts a comparative performance of BenevolentAI on Euronext Amsterdam starting 25.04.2022.TickerMean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$MBAI-0.55%3.27%-30.81w9.9MBenevolentAI StockTop AI in Pharma Best-Promising Companies in Q2-Q3 2022Schrdinger,Recursion Pharmaceuticals and Relay Therapeutics constitute the group of promising companies selected for analysis.They are new to the market(their IPOs closed in 2020).Therefore,their future might change significantly.Moreover,they have decent multi-target pipelines of novel therapeutics to address unmet medical needs.The companies are expected to translate their proprietary insights and technical solutions into effective therapeutics.Currently,the companies have a firm market position and thus receive high expectations from investors.NameCountryFunding Amount,$MIPO DateCapitalization,$BValuation at IPO,$MIPO Share Price,$Current Share Price,$EV/EBITDANet Income,$MSchrdingerUSA562.302.05.20202.2481917.0031.45-15.74-134.800Recursion PharmaceuticalsUSA208.517.07.20201.5151355.219.008.81-4.60-211.74Relay TherapeuticsUSA520.016.07.20202.06173620.0018.95-4.80-383,73456Deep Pharma IntelligenceStock Prices,USDAI in Pharma Corporations Financials57Deep Pharma IntelligenceAI in Pharma corporations tend to be more volatile than average publicly traded company.For most of the corporations,daily returns are positive and abnormal compared to the market.More volatile stocks are usually characterized by higher betas(both calculated for IBB index and for S&P 500).AI in Pharma segment is definitely a segment of growth stocks with the investors focused on the prospects of the companies rather than on the dividends.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB BetaS&P 500 BetaTotal Funding Amount,$MOperating MarginEV/EBITDANet Income,$MGritstone Oncology247.564-0.09%5.87%8,78$.54%0.519396-713.26%-0.36-111,921Lantern Pharma59.13-0.25%4.31%5.32%-7.05%1.081.3268.700.00%0.67-14.03Alector10780.24%4.18%5.77%-2.66%N/A1.34194.5011.95%-5.06-28.78Relay Therapeutics2144-0.06%5.27%5.67%-3.13%1.481.34520.00-10,056.81%-4.79-383,734Schrdinger2391-0.17%4.16.51.03%1.131.14567.20-79.25%-16.85-134,804Sensyne Health 790-0.83.44%2.755%1.590.8737.25-450.76%0.23-34,834Berkeley Lights356-0.61%6.63%-6.76%-9.52%1.59N/A272.60-88.44%-3.39-77,715LargeMediumLowAI in Pharma Corporations FinancialsDeep Pharma Intelligence58Market capitalization of some AI in Pharma corporations(such as Schrdinger)exceeds$6B whereas other companies are priced in the range of dozens of millions of dollars-the difference in the valuation is immense.There is no strong correlation between operating margin or net income and market capitalization-the valuation of the corporations still being unprofitable can exceed billion of dollars.Selling shares to investors allows them to maintain their cash burn ratios on an acceptable levels.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB BetaS&P 500 BetaTotal Funding Amount,$MOperating MarginEV/EBITDANet Income,$MBiodesix110-0.22%6.89%-10.85.91%N?A1.43289.70-162.47%-2.46-51,784C4X discovery78-0.01%3.18.75(.91%0.140.188.71-120.92%-7.71-4,721DeepMatter Group4.63-0.72%7.47%-5.89%-11.54%1.220.37N/A-323.44%-1.54-3,026eTherapeutics108-0.01%4.32.72&.25%0.350.9798.50-2,006.29%-8.73-8,070GenFit231.140.16%4.93.681.92%1.320.8393.6937.71%0.3367,25Biomea Fusion347.090.13%6.54%-14.26%-2.13%N?A0.3256.000.00%-2.86-60,940LargeMediumLowAI in Pharma Corporations FinancialsDeep Pharma Intelligence59Market capitalization growth of AI-driven Pharma corporations exceeds that of the entire market and general BioTech Industry indices represented as S&P 500 index and IBB,respectively.The difference is that compared to the general market,the AI-driven pharma market segment is more volatile.The distribution of the returns in the segment of AI-driven pharma companies is right-skewed,which differentiates it from the vast majority of stock indices and segments.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB BetaS&P 500 BetaTotal Funding Amount,$MOperating MarginEV/EBITDANet Income,$MBioXcel Therapeutics459.24-0.04%5.49%-5.89%8.13%1.181.03N/A0.00%-1.95-112,027Evolutionary Genomics4.63-0.06%4.51%6.44%0.00%-0.06-0.071.50.00%-4.81-3,090IDEAYA Biosciences608.192-0.10%3.92%2.48%8.68%1.361.47226.10-172.69%-6.49-56,839ITeos Therapeutics968.4840.12%4.04%8.42).24%1.500.73249.7477.35%0.29297,637Recursion Pharmaceuticals1737-0.29%5.87%5.56%8.47%N?A1.22465.38-1,608.40%-5.74-211,741Sangamo Therapeutics814.076-0.19%4.08%7.98%2.14%1.401.1493.20-157.09%-2.62-176,330Renalytix AI98.31-0.79%5.63%1.66%5.580%1.691.0576.40-1,922.86%-0.37-46,2Evaxion Biotech73.408 0.00%8.05.583.18%N?A0.9617.000.00-1.89-26,230LargeMediumLowTop Publicly Traded Companies Related to AI-PharmaDEEP PHARMA INTELLIGENCECompanies Related to AI-PharmaDeep Pharma Intelligence61AI in pharma sector is an integral part of the contemporary pharmaceutical industry.AI-Pharma sector,defined broadly,is not limited to AI companies,but includes also pharma,tech,chemistry corporations,and CROs that are engaged in collaborations with AI startups,including but not limited to:Mergers&Acquisitions,scientific researches,partnerships,and so on.Hence the companies chosen are better to be described as AI-related or AI-aiming than AI-based solely.The number of new partnerships between pharma companies and AI companies is ever increasing across the whole industry.On the one hand,AI-focused companies may spend a few years developing all software and tools which pharma companies do not have.On the other hand,large companies,mainly public ones,have solid understanding of their science,extensive experience in the industry and regulatory field,and they are ready to share the risk.In this chapter we introduce the list of top corporations related to AI-Pharma that were selected based on the analysis of their R&D,financials,and collaborations with the most promising and advanced AI-Pharma startups.Big Pharma CompaniesAI CompaniesBiotechnology CompaniesData Integration CompaniesGenetics CompaniesAI in PharmaPublicly Traded Companies Related to AI-PharmaDriven to some extent by the COVID-19 pandemic,publicly traded companies related to AI-Pharma demonstrated significant growth,reaching$14.13T industry capitalization as of the end of Q3 2022.Investors interest is being shifted towards industries of this nature.We see significant potential for Artificial Intelligence in the Pharmaceutical Industry.The Expected Compound Annual Growth Rate for this is market is projected to be around 40%over the next 3 years.The Biotechnology Industry is poised to witness a quantum leap soon,mainly because of the impact of Artificial Intelligence on biomedicine R&D.Many transactions are being announced,including Parexels acquisition for$8.5B,that indicates growing awareness of the disruptive potential in this sector for ones having the right means for participation.COVID-19 will continue to push valuations and M&A activity in the sector.Deep Pharma Intelligence62Cumulative Capitalization of Publicly Traded Companies Related to AI-Pharma,Q2-Q3 2022,$Billions$18T$16T$14T$12T1-June-20221-July-20221-August-20221-September-2022Top 10 Publicly Traded AI-Pharma Related Companies by Market Capitalization in 2022The chart represents the top-10 public companies that ended up in our portfolios according to their market capitalization.Johnson and Johnson,NVIDIA and Eli Lilly top our list,accounting 50.5%of the capitalization of all companies included.During the last year and a half period of pandemic,AstraZeneca has being raised the capitalization by more than 10 times,reaching$172B.63Deep Pharma Intelligence$500B$400B$300B$200B$100B$429B$311B$307B$307B$266B$237B$227B$218B$205B$172BRoche Holding(RHHBY)Roche Holding AG offers pharmaceutical products for treating anemia,cancer,cardiovascular,central nervous system,dermatology,hepatitis B and C,HIV/AIDS,inflammatory,autoimmune and other diseases.The company widely implements data-driven solutions,for example Roche has acquired Viewics,Inc.Viewics focuses on business analytics for laboratories,taking data from a variety of sources and extracting it to make faster data-driven decisions in operating processes in the labs.Novo Nordisk(NVO)Novo Nordisk is a healthcare company,engages in the research,development,manufacture,and marketing of pharmaceutical products worldwide.It operates in two segments,Diabetes and Obesity care,and Biopharm.Novo Nordisk actively implements different AI in Pharma solutions,its foundation awards DKK 138 million under its new data science and artificial intelligence initiative.Astrazeneca(AZN)Astrazeneca discovers,develops,manufactures,and commercializes prescription medicines in the areas of oncology,cardiovascular,renal and metabolism,respiratory,infection,neuroscience,and gastroenterology worldwide.Astrazeneca uses advancing genomics research with AI and big data,AI is already being embedded across companies R&D both for research and experiment optimization.AbbVie(ABBV)AbbVie is one of the so-called Big Pharma companies.The company uses AI not only for direct development but also for its own enhancement:Abbelfish Machine Translation and AbbVie Search are built for accelerating and scaling the work of the company researchers,reducing the time it takes to discover and deliver transformative medicines and therapies for patients.Top Publicly Traded Companies Related to AI-Pharma64Deep Pharma IntelligenceBerkeley Lights(BLI)Berkeley Lights is a leading Digital Cell Biology company focused on enabling and accelerating the rapid development and commercialization of biotherapeutics and other cell-based products for the customers.Besides 2 unique optofluidics system,Berkeley Lights is known for antibody discovery and cell lines development that definitely requires the usage of AI-powered algorithms and technical solutions.DeepMatter Group(DMTR)DeepMatter Group Plc operates as a big data and analysis company.It offers DigitalGlassware platform to deliver applications resulting in optimized chemicals,materials,and formulations in various areas,such as pharmaceutical research,fine chemicals,scientific publications,and teaching.The company develops and commercialises cheminformatics software to handle,store,and retrieve chemical structures and reactions for application in pharma;and tools for the production of synthesis planning and reaction prediction solutions,as well as engages in the automatic extraction of scientific information from text and images.Pharmaceutical Product Development(PPD)Pharmaceutical Product Development is another big CRO company.PPD ended up in our portfolio for a great reason,collaborating with Happy Life Tech for AI support,the company aims to create Data Science-driven Clinical Research Solutions in China to enhance global drug development.Charles River Laboratories(CRL)Charles River Laboratories is a well-known Contract Research Organization(CRO)specializing in research and drug development.CRL uses the AtomNet platform,which is a deep convolutional neural network created for structure-based drug discovery.The company also works with the Valence Discovery Platform for Hit-to-Lead acceleration and optimization and provides all research services considering these platforms.Top Publicly Traded Companies Related to AI-Pharma65Deep Pharma IntelligenceAgilent(A)Agilent is one of the biggest Biotech companies providing technical solutions for the Pharmaceutical industry.Lots of company technical solutions already have built-in or support different type of AI algorithms.Also,Agilent and Visiopharm co-promote advanced digital Precision Pathology Solutions.Thermo Fisher Scientific(TMO)Thermo Fisher is another,even bigger,Biotech company that is specializing in technical solutions,providing also a wide range of other services.“The connected Lab”is a good example of AI-enhanced services providing by the company,creating solutions for enhanced in-Lab performance via AI-based info-gathering and analysis.AI-based processing tools are now also available in Thermo Scientific Amira-Avizo Software and PerGeos Software.Johnson and Johnson(JNJ)Johnson and Johnson is considered o be among the TOP-3 biggest Pharmaceutical companies in the world,therefore not only implementation but also investing in AI in Pharma is provided by the company.In 2020,J&J announced an investment in Datavant Holdings,which is working to help healthcare organizations unite data across institutions to enhance medical research and patient care.Another JJI partner,Aetion Inc.,analyzes electronic medical records,insurance claims,patient registries and lab results to generate healthcare-related decisions.Almirall(ALM)Almirall is a leading skin-health focused global pharmaceutical company,that has some recent collaborations with Iktos for the creation of generative modelling AI technology for quick identification of molecules with multiple bioactivity and drug-like criteria.Top Publicly Traded Companies Related to AI-Pharma66Deep Pharma IntelligenceAI for Advanced R&D:Applications and Use CasesDEEP PHARMA INTELLIGENCENotable AI Breakthroughs68Deep Pharma IntelligenceIBM Watson released a cognitive computing platform for Clinical trial matching that has shown significant improvement in patient enrollment rate at Mayo Clinic.The platform demonstrated an 80%increase in enrollment in clinical trials for breast cancer and a decrease in time to match a clinical trial to one patient.Healx has prepared a rare disease Fragile X syndrome drug for a Phase 2a clinical trial in 15 months.Healx has demonstrated the power of combining domain expertise,deep learning,and proprietary data.DeepMind built the AlphaFold platform to predict 3D protein structures that outperformed all other algorithms.AlphaFold won the CASP13 competition,where it could most accurately predict the shape for 25 of the 43 proteins without using previously solved proteins as templates.Recursion Pharmaceuticals has evaluated Takedas preclinical and clinical molecules in over 60 indications in less than 18 months by Recursions AI-enabled drug discovery platform.Insilico Medicine has published a research paper about the first in vivo active drug candidate developed from scratch(de-novo)in 46 days(with target selection)using the GENTRL AI-based system.Oct 2018Mar 2018Dec 2018Sep 2019Jan 2019Notable AI Breakthroughs69Deep Pharma IntelligenceDeep Genomics created a DG12P1 drug in 18 months using an AI-augmented drug design.It is an antisense oligonucleotide therapy to treat rare Wilson disease.Deed Genomics platform screened over 2,400 diseases and over 100,000 mutations to predict and confirm the precise disease-causing mechanism of the mutation Met645Arg.Mendel Recruit proprietary platform increases patient enrollment for clinical trials by 24-50%.It applies AI algorithms that combine the recognition of scanned documents with natural language processing of clinical records and automated clinical reasoning.A new drug candidate,DSP-1181,created using the Exscientia Centaur Chemist Artificial Intelligence platform,began clinical study.The drug was developed together with Sumitomo Dainippon Pharma for the treatment of an obsessive-compulsive disorder.It was advanced to Phase 1 clinical trials.Scientists from MIT discovered halicin a new super powerful antibiotic capable of killing 35 of the worlds most problematic disease-causing bacteria,including multiresistant strains.The model applied was able to screen more than a hundred million chemical compounds and pick out potential antibiotics that kill bacteria using different mechanisms than existing drugs.Aladdin has built a platform for the early diagnostics of Alzheimers disease and COVID-19.Disease Diagnosis platform uses AI and multimodal data,including biomarkers,imaging,blood samples,medical records,etc.Jan 2020Sep 2020Sep 2019Feb 2020Jan 2020Notable AI Breakthroughs70Deep Pharma IntelligenceMELLODDY the Machine Learning Ledger Orchestration for Drug Discovery group was created by ten pharma companies to develop ML models without sharing data.MELLODDY leverages the worlds most extensive collection of small molecules with known biochemical or cellular activity to provide more accurate predictive models and improve drug discovery efficiency.Insilico Medicine achieved industry-first nominating Preclinical Candidate.The company performed all the required human patient cell,tissue,and animal validation experiments to claim a first-in-class preclinical candidate for a novel pan-fibrotic target.The company is preparing for clinical development.Cyclica launched an AI-based drug discovery platform that achieved over 60%of actionable hits for its pharma clients.Cyclica has partnered with over 100 global pharma and biotech companies and academia across many therapy areas.These partnerships have resulted in 64%of programmes resulting in actionable hits.BioXcel Therapeutics,Inc.,a clinical-stage biopharmaceutical company utilizing AI approaches,announced that the FDA has accepted for filing the New Drug Application for BXCL501,for the acute treatment of agitation associated with schizophrenia and bipolar disorders I and II.Using its AI technology,Exscientia designed an Alzheimers disease drug candidate who has entered Phase I clinical testing.The AI-designed drug candidate will be assessed for improved antipsychotic effects associated with Alzheimers psychosis,in addition to improvements in behavioural and psychological symptoms of dementiaSep 2020Mar 2021May 2021Feb 2021May 2021Notable AI Breakthroughs71Deep Pharma IntelligenceThe University of Washington has developed a deep learning model,“RoseTTAFold”,that calculates protein structure on a single gaming computer within ten minutes.Insilico Medicine announces the preclinical candidate for kidney fibrosis discovered using end-to-end Artificial Intelligence engine.The preclinical candidate has the desirable pharmacological properties,pharmacokinetic profile and demonstrated auspicious results in in-vitro and in-vivo preclinical studies.Exscientia,in cooperation with the Medical University of Vienna,published a paper that illustrates the potential real-world impact of using Exscientias AI-supported precision medicine platform.The platform proposes the most effective therapy for late-stage haematological cancer patients based on testing drug responses ex vivo in their own tissue samples.AstraZeneca,Merck,Pfizer and Teva formed AION Labs,the innovative lab that will create and adopt AI technology to transform the process of drug discovery.AION Labs will create and invest in companies that implement AI for drug development.Additionally,they will offer special resources and mentorships to such companies.The AI-empowered company Healx has secured FDA approval for a phase 2a clinical trial of an AI-discovered compound that could help manage the symptoms of the genetic disorder Fragile X syndrome.Jul 2021Oct 2021Oct 2021Oct 2021Aug 2021Standigm had established a Synthetic Research Center in the headquarters of SK Chemicals Co.,Ltd(SK Chemicals,KRX 285130),a life science and green chemicals company.Notable AI Breakthroughs72Deep Pharma IntelligenceInsilico Medicine,an end-to-end artificial intelligence(AI)-driven drug discovery company,announced that the first healthy volunteer has been dosed in a first-in-human microdose trial of ISM001-055.BenevolentAI,a leading clinical-stage AI drug discovery company,announced that AstraZeneca had added a novel target for idiopathic pulmonary fibrosis(IPF),discovered using BenevolentAIs platform,to its drug development portfolio.This is the second novel target from the collaboration that has been identified,validated,and selected for AstraZenecas portfolio.Lantern Pharma presented positive data on the effectiveness of LP-284 in hematologic cancers at the 63rd American Society of Hematology(ASH)Annual Meeting.Erasca announced the FDA has cleared an investigational new drug application for ERAS-801,an orally available small molecule epidermal growth factor receptor inhibitor specifically designed to have high central nervous system penetration for the treatment of recurrent glioblastoma multiforme.Dec 2021Dec 2021Dec 2021Nov 2021Nov 2021Notable AI Breakthroughs73Deep Pharma IntelligenceAbCellera and its collaborators released new preclinical data showing the pseudovirus neutralization status of its two monoclonal antibodies,bamlanivimab and bebtelovimab(also known as LY-CoV1404),against the Omicron variant.Bristol Myers Squibb announced the CMPH of the EMA has recommended approval of Breyanzi,a CD19-directed chimeric antigen receptor T cell therapy for the treatment of adult patients with relapsed or refractory(R/R)diffuse large B-cell lymphoma(DLBCL),primary mediastinal large B-cell lymphoma(PMBCL),and follicular lymphoma grade 3B(FL3B)after two or more lines of systemic therapy.AI Therapeutics announced the initiation of a Phase II study for a promising new approach to treat amyotrophic lateral sclerosis(ALS).Aizon announced the launch of its new asset monitoring application for pharmaceutical manufacturers and biotech companies.Built on Aizons GxP compliant AI SaaS Platform,Aizon Asset Health provides intelligent historical maintenance analysis,proactively monitors the condition of critical assets in real time,and provides actionable maintenance recommendations that keep equipment up and running optimally.Cyclica launched Perturba Therapeutics-a spin out from the Stagljar Lab at the University of Toronto,Donnelly Centre for Cellular and Biomolecular Research.Perturba is advancing a rich pipeline of assets from undrugged protein-protein interactions.Feb 2022Feb 2022Feb 2022Jan 2022Jan 2022Notable AI Breakthroughs74Deep Pharma IntelligenceThe US FDA has officially approved Niramai Health Analytixs first product,which is used to provide an innovative radiation-free,non-touch,accurate breast cancer screening solution.A breast thermography tool aids medical professionals in reviewing,measuring,and analyzing thermally relevant indications in the breast regionThe purchase of TARA Biosystems,a biotech business focused on cardiovascular illness,by Valo Health has created the first vertically integrated platform for the development of cardiovascular drugs.The combination of TARAs unique human 3D tissue engineering technology and Valos Opal Computational PlatformTM allows Valo to revolutionize the research and development of drugs for cardiovascular diseases.The FDA has given Breakthrough Device Designation to Anumana,Inc.,an AI-driven health technology firm from nference,Inc.,for its AI-enhanced,ECG-based Pulmonary Hypertension(PH)Early Detection Algorithm.The algorithm is a precise,screening tool for earlier diagnosis of patients with pulmonary hypertension.Aizon wins the 2022 Artificial Intelligence Breakthrough Awards Programs Best AI-based Solution for Manufacturing Award.The FDAs gave Biogen and Eisais follow-up to the Alzheimers disease medication Aduhelm priority review status.The businesses are aiming for a quick assessment of their anti-amyloid medication lecanemab,which can replace the contentious Aduhelm.May 2022Jul 2022Jun 2022Apr 2022Mar 2022DEEP PHARMA INTELLIGENCEComputational Methods Used by the Most Advanced AI CompaniesComputational Methods Used by the Most Advanced AI Companies76Deep Pharma IntelligenceNatural Language ProcessingComputational MethodsMachine LearningDeep LearningChemoinformaticsBioinformaticsSymbolic AIReinforcement LearningGANsQuantum ComputingCNNEvolutionary AlgorithmsFederated LearningCompanyComputational methods usedTechnology AbstractBioinformatics,Deep Learning,NLPArdigen is active in the field of laboratory information management systems,biological and clinical data analysis,Big Data integration,as well as custom application development.Machine Learning,Deep Learning(Convolutional neural networks),chemoinformaticsAtomNet is the first drug discovery algorithm to use a deep convolutional neural network.It has already explored questions in cancer,neurological diseases,antivirals,antiparasitics,and antibiotics.NLP,Deep Learning,Machine LearningDecodes open-and closed-access data on reagents such as antibodies and present published figures with actionable insights.Machine Learning,Deep Learning,symbolic AI,chemoinformaticsEvolved from text mining and semantic linking into knowledge graphs to tackle complex multifactorial diseases,identify novel targets,small molecule drug discovery and patient stratification.Machine Learning,Deep Learning,bioinformaticsAnalyze data from patient samples in both healthy and diseased states to generate novel biomarkers and therapeutic targets.Machine Learning,bioinformaticsAutomate selection,manipulation,and analysis of cells.Allows researchers to:Expedite development of cell lines and automate manufacturing of cellular therapeutics.Computational Methods Used by the Most Advanced AI Companies77Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractNLP,Deep Learning,Machine LearningProcess raw phenotypic,imaging,drug,and genomic data sets.Allows researchers to integrate rapid analytics and machine learning capabilities into existing business processes.NLP,Deep Learning,Machine LearningBioz has developed a search engine for Life Sciences community using natural language processing and machine learning technology to scan hundreds of millions of pages of complex and unstructured scientific papers on the web.Machine Learning,Deep Learning,chemoinformaticsBioxcel Corporation is a biopharmaceutical company pioneering the application of artificial intelligence and big data analytics integrated with drug development expertise.Machine Learning,Deep Learning,chemoinformatics,bioinformaticsC4X innovative DNA-based target identification platform(Taxonomy3(R)utilises human genetic datasets to identify novel patient-specific targets.Deep Learning,BioinformaticsIt is a deep learning company that uses innovative,computer-based methods to degrade undruggable targets and validate lead drug candidates in automated labMachine Learning,Deep Learning,symbolic AI,chemoinformatics,bioinformaticsCytoReasons access to unmatched proprietary and public data,combined with cutting-edge machine learning technologies,creates their unique biological models of disease,tissue and drug.Computational Methods Used by the Most Advanced AI Companies78Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,Deep Learning,NLPThe Data4Cure platforms modular architecture allows independent system components to handle integration and advanced analysis of heterogeneous data types spanning molecular,phenotypic and clinical data,both structured and unstructured.Machine Learning,Deep Learning,bioinformaticsDeep Genomics is using artificial intelligence to build a new universe of life-saving genetic therapies.Bioinformatics,Machine LearningDesktop Genetics is team of genome editing experts,bioinformaticians and data scientists,driven by the real-world impact of CRISPR technology.Their core technology,DESKGEN AI,was trained on the largest database of genome editing data in the world.Machine Learning,Deep Learning,high-performance computingEnvisagenics SpliceCore platform integrates proprietary machine learning algorithms,high performance computing,and RNA-splicing analytics to identify disease-specific alternatively spliced RNA that will function as therapeutic targets.Machine Learning,Deep Learning,bioinformaticsEuretos provides direct access to the cloud based discovery platform via user friendly application and also allows integration of company proprietary data and public data in a secure environment.Machine Learning,Deep Learning,bioinformatics,chemoinformaticsThe company uses ML for predicting ADME,novelty,synthetic accessibility,pharmacology of molecules.Computational Methods Used by the Most Advanced AI Companies79Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,Deep LearningBlending computational biology and AI-based methods,Genialis merges and models data at the intersection of clinical and translational medicine.Machine Learning,Deep LearningGNS Healthcare AI technology integrates and transforms a wide variety of patient data types into in silico patients which reveal the complex system of interactions underlying disease progression and drug response.Machine Learning,NLP,symbolic AI,chemoinformatics,bioinformaticsHealx AI platform uses natural language processing to extract disease knowledge from published sources and to complement biomedical databases and proprietary,curated data.Machine Learning,Deep Learning,cheminformaticsIktos has invented and is developing a technology based on DL for ligand-based de novo drug design,focusing on multi parametric optimization(MPO)Deep Learning,GANs,GANs Reinforcement Learning,symbolic AI,Machine Learning,chemoinformatics,bioinformaticsComprehensive DL pipeline.Biology:Signaling pathways,DNNs for target ID and HTS analysis.Chemistry:GANs-RL for novel molecule generation.NLP,Deep Learning,Machine LearningKyndi provides leading artificial intelligence software that can analyze long-form text and find actionable insights in a smarter,faster,and more explainable way.Computational Methods Used by the Most Advanced AI Companies80Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,chemoinformaticsWith a huge experience in Lead Generation,Lead Optimisation and method development the goal of the company is to accelerate the progress of our clients programmes.NLP,Deep LearningnferX uses state-of-the-art Neural Networks for real-time,automated extraction of knowledge from the commercial,scientific,and regulatory body of literature.Big data analytics;Deep Learning,Machine LearningDiscover connections between drugs and diseases at a systems level by analyzing of millions of raw human,biological,pharmacological,and clinical data points.Deep Learning,BioinformaticsPredict the therapeutic potential of food-derived bioactive peptides.Allows researchers to:cost-effectively develop highly targeted treatments for specific diseases from natural food sources.Machine Learning,Federated LearningOwkin combines the expertise in biology and machine learning to fuel precision medicine.Owkin facilitates access to real-world data by therapeutic area through its data connect service.Deep Learning(TensorFlow Keras base)Worlds first protein database specifically for Deep Learning and AI applications with full Keras and Tensorflow integration.Computational Methods Used by the Most Advanced AI Companies81Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractDeep Learning,Reinforcement LearningPhenomic predicts which cells will survive chemotherapy and identifies compounds that selectively target these resistant cells.It will then develop the compounds and bring them to market.Quantum Computing,Reinforcement Learning,ChemoinformaticsProteinQure is combining quantum computing,reinforcement learning,and atomistic simulations to design protein drugs.They can design peptide-based therapeutics and explore protein structures without crystal structures.Evolutionary algorithms,Machine LearningML-based structure based predictive models for potency and ADMET/PK properties of small molecules.Machine Learning,Deep LearningReviveMeds platform enables the rapid,high-throughput,and cost-effective application of metabolic data to discover new disease mechanisms for drug discovery and,simultaneously metabolomic biomarkers to identify which patients stand to benefit by targeting the disease mechanism.Machine Learning(stochastic gradient descent and branch-and-bound maximum likelihood optimization)The cryoSPARC System enables high-throughput structure discovery of proteins and molecular complexes from cryo-EM data with help of machine learning.Quantum physics;Machine LearningXtalPis ID4 platform provides accurate predictions on the physiochemical and pharmaceutical properties of small-molecule candidates for drug design,solid-form selection,and other critical aspects of drug development.Computational Methods Used by the Most Advanced AI Companies82Deep Pharma IntelligenceDEEP PHARMA INTELLIGENCE15 Notable R&D Use Cases of AI Application in BiopharmaPharmaChemical synthesisSmall drugs molecules 84Deep Pharma Intelligence2 Key AdvantagesAIMachine LearningDeep LearningCognitive Reasoning TechnologiesNatural Language ProcessingBiopharma utilizes living organisms(such as yeasts,bacterias,and mammalian cells)which are capable to produce complexly structured products such as proteins,hormones,RNA and DNA products,and virus capsids.Whereas Pharma relies on a classical chemical synthesis producing small drug molecules.However,both industries may benefit from AI-driven applications.To develop new small drug molecules or biologically-derived products,AI-driven data processing serves as a tool that allows minimising time consuming biological testings while helping to select the most promising products to test.BiopharmaLiving organisms(yeasts,bacterias,mammalian cells)Complex structures(proteins,DNA,RNA,hormones,viruses capsids)Introduction to Most Innovative R&D Approaches of AI in Biopharma Most Innovative R&D Approaches of AI in Biopharma.Strados Labs85Deep Pharma IntelligenceStrados Labs enters the Pharma and Life Science market with a Respiratory Management Solution that includes the only FDA-cleared,RESP biosensor which acquires lung sound acoustics wireless and hands-free,making it a perfect fit for clinical research to measure patient response to new drugs by objectively collecting coughs and other lung sounds discreetly,comfortably,and securely in a streamlined way,while having access to data for post-processing and analysis.Cough Trial SolutionTrial Partner SolutionDecentralized Trial CompatibilityDigital Acoustics Biomarkers*Listed companies are industry leaders and prospects for StradoslabsHow Strados Labs Uses AI in R&D?220 hours of continuous data collection without patient intervention of objective lung sounds and respiratory dynamics while having access to data for post-processing and analysis.Noise cancellation is applied to enhance the signal to noise ratio and eliminate speech discernibility while being HIPAA compliant with an end to end encryption.Data collected via RESP is uploaded automatically to the Strados Cloud to allow assessment of recordings timely with identification of adventitious breath sounds including respiratory dynamics with ML algorithms.Wireless,non invasive biosensor that monitors,records and stores every lung sound.That translates into longer wear times and an astounding 99.59%patient compliance.Identification of wheeze,cough,and CABS detection gives the objective measurement of these changes over time on a patient and population basis with an ability to differentiate cough types in addition to frequency.Strados Labs a respiratory management solution,which brings innovation at the intersection of lung biomarkers,patient centricity,and machine learning.The industry of life sciences can largely benefit from the enhancement of pulmonary care monitoring capabilities provided by Strados Labs to gain insight into patient drug response by analysis of longitudinal lung acoustics.Data Collection Capacity Patient Privacy&SecurityReal-Time Data AnalysisPatient CentricityLongitudinal Lung Data86Deep Pharma IntelligenceHow Strados Labs Uses AI in R&D?Today Strados Labs has a unique opportunity to stand as a leader in Respiratory Health:their clinically validated bioacoustic library of sounds and AI engine is the worlds largest entirely hands-free,clinical-grade dataset enabling Strados Labs to be the standard bearer of acoustic digital biomarkers for clinical research and respiratory care globally.The Strados Respiratory Management Solution is the worlds first FDA-cleared lung sound platform with a proprietary wireless biosensor,RESP,that is passive,patient-friendly,and clinically validated to acquire lung sounds in the real world.For instance,Strados Labs RESP fits perfectly into decentralized trials allowing remote patient access by unlocking lung sound data and putting it into the hands of the entire research team via the cloud.Making decentralized respiratory trials scalable and able to develop entirely new insights about respiratory status without episodic patient interaction.Strados Cloud:companys passive and longitudinal bioacoustics insights allow them to build a more complete picture of the subjects respiratory status leading to better trial outcomes.87Deep Pharma IntelligencePatientStrados CloudStrados Clinical PortalStrados AISolutionsHow Standigm Accelerates Drug Discovery using AINovel TargetsIdentification First-in-ClassLead GenerationCataloged AssetsTailored Partnership ModelsStandigms AI solution Standigm ASKTM provides novel targets perfectly fit to a customers research context within two weeks.Standigms optimized workflow AI system can generate multiple First-in-Class compounds within seven months.Standigm has an exceptional reservoir of ready-made in-house therapeutic assets,which are as attractive as to meet customers pipeline needs.Therapeutic areas of assets:Standigm has tailored partnership models perfectly fit to a customers needs,from licensing of AI platform and assets to AI solution providing.Standigms partnership models:Licensing of Standigms AI Platform(Standigm ASKTM,Standigm BESTTM)Licensing of First-in-class assets driven by Standigms AI platformProviding Standigms AI solution88Deep Pharma IntelligenceCancerNASHParkinsons DiseaseMitochondrial DiseaseIntegrated workflow AINovel target Identification(Stangdim ASKTM)Novel compound design(Stangdim BESTTM)Novel target IdentificationQuery:DiseaseStangdim ASKTMOutput:Novel Target123Standigm is a workflow AI-driven drug discovery company headquartered in Seoul,South Korea and subsidiarized in Cambridge,UK.Standigm has proprietary AI platforms encompassing novel target identification to compound design,to generate commercially valuable drug pipelines.The company has established an early-stage drug discovery workflow AI to generate First-in-Class lead compounds within seven months.o date,Standigm is running 42 in-house or collaborative pipelines for drug discovery using the workflow AI technology.One of the companys pipelines is expected to enter a pre-clinical stage in 4Q 2021.How Standigm Accelerates Drug Discovery using AIStandigm ASKTM is a customizable,AI-aided drug target identification platform,prioritizing disease-target relationships and providing evidence-based results through an interactive user interface.Standigm BESTTM is a novel compound generation platform,which can investigate lead compounds whenever target or ligand information is lacking or enough.89Deep Pharma IntelligenceDatabaseHit IDHit to LeadLead OptimizationGraph DBPrioritization AlgorithmMulti FiltersNovel Target SelectionHow Standigm Accelerates Drug Discovery using AITarget Proposal(Patent Analysis)Purchasing Compound,Assays,Hit ScreenStandigm Releases First-in-Class Compounds within 7 MonthsFeatured Partners90Deep Pharma IntelligenceStandardized workflowNovel target Identification(Stangdim ASK)Novel compoundDesign(Stangdim BEST)7 months(rather than years/Standigm)30 months(Traditional Approach)Synthesis(Only top 30 compounds)In vitro assay(potency,microsomal stability,hERG)First-in-classcompounds1MCollaborator(Pharma Company):3M(Hit Compounds)Stangdim BESTTMData Collection,Analysis*and Model Validation*3M(Hit Compounds)3M(Hit Compounds)Preclinical CandidateStandigm made the hit-to-lead stage with a cancer Target A within 7 Months*Data Analysis Binding site analysis using protein structure*Model Validation Validation of activity prediction models:ChemMap-based,2D structure QSAR-based,Simulation-based and Ensemble-based methods1M1st Round Commercial Compounds(Library of x million)1M2nd Round Commercial Compounds&Hit-Based Design1MSAR-Based Novel DesignChemical Synthesis,Assays,Hit SelectionChemical Synthesis,Assays,(In Vitro&In Vivo),Lead SelectionLead OptimizationIn Vivo&Tox3M(Hit Compounds)Most Innovative R&D Approaches of AI in Biopharma.AntiverseAntiverse is a new type of antibody discovery company accelerating drug development.The Antiverse platform exists at the intersection of structural biology,machine learning and medicine to enable breakthroughs to happen more quickly and cost-effectively.Library DiversityExploring the Full Functional Spacing Antiverse prevents diversity loss during amplification to uncover more diverse and rare antibodies.Antiverse provides more candidates by analysing NGS data,clustering on multi-dimensional space,and selecting based on sequential and structural grouping.The generative module offers new sequences and gives you options that havent even been considered.Traditional in vitro screening:Antiverse discovery:1010 antibodies3 amplification rounds10 antibodiesAntigen-antibody database96 antibodiesAI-augmented screeningAntiverse AI-Augmented Discovery:Recovery ModuleGenerative Module96 antibodies91Deep Pharma IntelligenceAntigen-antibody databaseAntiverse is recognized as one of the top biotech startups in the UK with our antibody discovery service already in use by big pharma.The main feature of the company is 10 x Diversity with AI-Augmented Drug Discovery.How Antiverse Engineers the Future of Drug DiscoveryExisting antibody discovery methods are well-developed and often effective at discovering binders.But when there is a need to find the best possible candidate,or when finding a suitable candidate is hard with current methods,the options are limited and often costly.Antiverse uses next-generation sequencing(NGS)to extract more data from existing workloads.The AI-Augmented Drug Discovery platform and trained models analyse the statistics gained from thousands of experiments.These outputs are compared against known data in order to select best candidates.Binder CustomisationAntiverse can generate new binder variants that will be sufficient for clients purposes.Target SelectionAntiverse provides targeted options in order to focus on testing safely once there are too many antibody-antigen binding options.Binder Recovery Antiverse can help to find sufficient potential binders that can be missed by conventional methods.92Deep Pharma IntelligenceThe Antiverse AI-ADD system found each and every cluster identified by other methods,plus more.These additional clusters contained rare and unique sequences.How Antiverse Uses AI in R&DBindersNGSDataAntiverse sends the sequences to Customer.Customer tests scFvs recovered/generated using either two-step linker PCR*or synthesise93Deep Pharma IntelligenceNGS FacilityThe Drug Discovery Ecosystem is Evolving Rapidly-And Data is at the Core.Arctoris is one of them:a biotech platform company with operations in Oxford,Boston,and Singapore,leveraging its fully automated platform for drug discovery.LIFE SCIENCE AUTOMATIONLIFE SCIENCE DATA MININGContract research organizationsSynthetic biologyData-powered drug discoveryLarge pharma/traditional biotechCloud laboratoriesAI drug discoveryDrug discovery is undergoing massive and rapid change-the rise of Artificial Intelligence and Machine Learning for Drug Discovery and the evolution of robotics-centric companies in the biomedical research space has enabled a new generation of companies to emerge:data-powered drug discovery companies that combine automation and data science.94Deep Pharma IntelligenceThe company was founded by an oncologist and a medicinal/synthetic chemist,with the goal to accelerate the discovery and development of new therapies by harnessing the power of technology and combining it with deep industry expertise.The core thesis of the company is that better data leads to better decisions,and that in order for drug discovery programs to develop and meet the next milestone faster and with higher chance of success,the underlying data must be rich,reliable,and reproducible.According to Arctoris,the status quo is no longer enough:in order to develop the best drugs,industry leaders have to rethink how they can improve their decision-making,powered by better data.Having developed a suite of proprietary technologies across robotics and data science/AI/ML,Arctoris is a leader in this new and rapidly evolving field.How Do Robotics and AI/ML Synergize in Drug Discovery?Both quality and speed are achieved by combining precision robotics with a unique data science platform and world-class drug discovery expertise from biotech and pharma veterans.Arctoris tracks all experimental outputs in full depth,including the capture and analysis of extensive metadata temperature,humidity,CO2,reagent provenance and batch ID among many others.At the same time,the platform enables automated QA/QC processing,applying statistical tools to ensure full reliability and validity of all results.Thereby,Arctoris ensures superior data to be generated in accelerated timeframes,leading to better decisions taken earlier-in human-powered but especially in AI/ML-driven programs,thanks to training of AI models with the best possible data.Taken together,Arctoris has developed a unique technology platform based on robotics and data science that powers drug discovery programs both in the companys internal pipeline and in partnerships with biotech and pharma companies worldwide.95Deep Pharma IntelligenceINDUSTRY-STANDARD DATA GENERATION&PROCESSINGARCTORIS-ENABLED DATA GENERATION&PROCESSING Widespread lack of reproducibility Unclear reagent and cell line provenance Inconsistent use of methods&protocols Human error&variability Only collection of high-level results data Highly fragmented file&storage systems Strict adherence to automated protocols Fully verified reagents and cell lines with complete audit trails Reproducible results data in standardized structure Additional collection of rich research meta-data Secure&convenient data storage&access Advanced assay performance monitoringThe greatest challenge in AI-driven and ML-powered drug discovery is access to well structured,fully annotated,reproducible and robust data.Arctoris leverages the power of robotics to generate vast amounts of ML-ready data that enable better decisions-thereby significantly accelerating timelines from target to hit,lead,and candidate.The Arctoris Platform:Leveraging Robotics&Data Science from Target to Candidate.Target ValidationHit finding&Hit-to-LeadLead OptimizationAnalysis of target expression and target half-live by quantifying protein turnover and route to degradationInvestigation of target function(changes in phenotype,pathways,gene expression,etc.)via cell-based and molecular biology readoutsAdvanced insights into effects of target modulation by employing complex model systems such as organoids,primary cells,etc.Machine-learning guided screening set selection and hit evolutionIn silico and in vitro screening and profilingBiophysical screening/profiling and FBDDRapid synthetic hit expansion and diversification incl.use of CADDKinetic and mechanistic biochemistry/enzymology and biophysical quantitation of target engagement energetics&kinetics Protein science and(co)crystallography for SBDDRapid biochemical profiling,kinetics,selectivity,mechanism of actionIsolated and in-cell target engagementCellular mode of action,elucidation of pathway modulation,confirmation of on-target/off-target effectMedicinal and synthetic chemistry(optimizing SAR,SPR,STR)Integration of synthetic and computational chemistry as well as in vivo ADMET for late-stage lead optimization Pharmacokinetics and pharmaco-dynamics(PK/PD)&safety pharma-cologyIn-depth pharmacokinetics,including ADME,drug-drug interactions,metabolite profiling,concentration time profilesComprehensive acute toxicology assess-ment,incl.single dose and repeated dose to determine MTD and NOAELAdditional toxicology studies(e.g.repro-ductive and developmental toxicity,etc.)Preclinical96Deep Pharma IntelligenceGenomenon is an AI-driven genomics company that organizes the worlds genomic knowledge to accelerate the diagnosis and development of treatments for genetic disease.Genomenons Prodigy Genomic Landscapes deliver a profound understanding of the genetic drivers and clinical attributes of any genetic disease and support the entire drug development process,from discovery to commercialization.Genomenons main focus therapeutic areas are rare diseases,genetic diseases,and hereditary and somatic cancers.Most Innovative R&D Approaches of AI in Biopharma.Genomenon97Deep Pharma Intelligence2 Key AdvantagesProdigyIdentify molecular drivers of diseaseEvaluate market potentialAccelerate natural history studiesIdentify genomic biomarkers for trial Identify CDx inclusion criteriaIdentify patients for clinical trials/approved therapiesDrug DiscoveryTrial OptimizationPatient IdentificationGenomenons Prodigy Genomic Landscapes use a unique combination of proprietary Genomic Language Processing(GLP)and expert,scientific review to provide an evidence-based foundation for all stages of the drug development process.These landscapes can be completed at the disease,gene,variant,or patient level,and are maximally comprehensive as a result of GLP.Genomic Landscapes are also rapidly produced using an AI-assisted curation engine that expedites manual review of the data indexed by GLP.Genomic Language Processing(GLP)is a novel technology that systematically extracts and standardizes genomic and clinical information from the medical and scientific literature.Designed specifically to recognize this complex genomic information,GLP provides superior sensitivity compared to traditional methods,finding more variants and subsequently,more patients.Genomenons database,built using GLP,currently contains over 14.8 million variants,8.8 million full-text articles,and 3 million supplemental datasets.How Genomenon Uses AI in R&D98Deep Pharma Intelligence99Deep Pharma IntelligenceHow Genomenon Uses AI in R&DATP7B gene associated with Wilson diseaseGenomenons AI-driven ApproachClinVar Crowd-sourced DatabaseSearch for mutations 634 Pathogenic Variants235 Pathogenic VariantsGenomenons AI-driven approach identified 3.7x more evidence-supported,pathogenic/likely pathogenic variants for ATP7B than ClinVar.We predict that this will improve the diagnosis of people living with Wilson disease by improving the ability to interpret genetic testing results.In collaboration with Alexion,AstraZenecas Rare Disease group,Genomenon applied its AI technology to help accelerate the genetic diagnosis for rare disease patients.Genomenons novel combination of AI-powered Genomic Language Processing and expert review identified significantly more pathogenic variants associated with Wilson disease.Genomenons AI-driven approach identified 3.7x more evidence-supported,pathogenic/likely pathogenic variants for ATP7B a gene associated with Wilson disease compared to the crowd-sourced database,ClinVar.This significantly expands the resources available to healthcare providers to make more informed diagnostic decisions.With greater adoption of Mastermind,we predict that the substantial increase in the number of known,disease-causing variants will improve the diagnosis of people living with Wilson disease by improving the ability to interpret genetic testing results.ATP7B gene associated with Wilson diseaseSearch for mutations Genomenons AI-driven ApproachClinVar Crowd-sourced Database869 Pathogenic Variants235 Pathogenic VariantsGATC Health has an unprecedented technology that will lower costs and accelerate the drug discovery and development process to create better and safer drugs,faster.The company delivers highly efficient services for pharma companies reducing the risk in the drug discovery process.GATC Health develops an end-to-end drug development cutting-edge AI-based platform with capabilities that include:earlier disease detection,identification of the disease biology,creation of new drug and therapeutic solutions,simulation of in-silico clinical trials and providing a feedback loop for in-vitro and in-vivo testing.GATCs Platform combines massive volumes of disease-specific data and proprietary AI solutions to replicate human biologys billions of interactions for rapidly and accurately discovering and validating novel drugs.This is a revolutionary approach to drug discovery that can address nearly any condition,disease or disorder;while drastically improving costs,efficiency and time for clinical development.De-Risking and Accelerating Drug Discovery&Development for Improved Success in Biopharma.GATC Health100Deep Pharma IntelligenceTarget Diseases Oncology NeurologyPsychiatrySubstance Abuse Cardiology Immunology Rheumatology Other diseasesOutcome 1-3 Small Molecules or Biologics Optimized for Specific Diseases and Patients2 Key AdvantagesDrug Discovery Drug Validation Drug Efficacy Prediction Human-like Molecular ModelsDisease State Specific Models 101Deep Pharma IntelligenceHow GATC Health Uses AI in R&D Develop new therapeutics using in-silico and in-vivo clinical studies with more comprehensive analysis.Ensure higher levels of success as the drug progresses through FDA trials.Eliminate majority of the risk and cost associated with t
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