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1、Life Sciences PracticeAI in biopharma research:A time to focus and scaleBy focusing on specific scientific and operational pain points and fully integrating AI into research workflows,biopharma companies can deliver greater patient impact and significant value.October 2022 Janiecbros/Getty ImagesThi

2、s article is a collaborative effort by Alex Devereson,Erwin Idoux,Matej Macak,Navraj Nagra,and Erika Stanzl,representing views from McKinseys Life Sciences Practice.Despite recent advancements,1 biopharma research in drug R&D remains expensive and time-consuming,although there are numerous opportuni

3、ties to build capabilities that enhance productivity and provide probability-of-success gains.In this time of rapid growth of AI in biopharma,attention today is on how to make the most of the opportunity to deliver value at scale by fully integrating AI approaches into scientific process changes.In

4、this article,we outline how biopharma companies can harness AI-driven discovery to deliver patient benefit,and why now is the time for a shift from pursuing select marquee partnerships and self-contained capability builds,to focusing on coordinated investment in research AI with impact to show for i

5、t.The goal of the research phase in drug R&D is to generate as many quality drug candidates as possible,as quickly as possible,with the highest probability of successful transition to clinical development.The discovery process has historically been a convergent,stepped,passfail funnel process with a

6、ttrition at every stepa process that is highly inefficient given the number of compounds initially tested.2 Ideally,this process should only promote compounds for testing that are relevant for targets that would lead to effective drugs for patients.AI can help identify the most promising compounds a