1-5 分布外鲁棒图学习的一些新进展.pdf

1、SOME ADVANCES IN OUT-OF-DISTRIBUTION GRAPH LEARNINGYatao Bianhttps:/ AI Lab|01DrugOOD:A testbed for graph OOD learning02Subgraph based invariant graph learningCONTENT|DrugOOD:Background01|Drug Discovery is a Long and Experience Process|It takes more than 10 years and$1B to develop a new drugGaudelet

2、,T.,Day,B.,Jamasb,A.R.,Soman,J.,Regep,C.,Liu,G.,.&Taylor-King,J.P.(2021).Utilizing graph machine learning within drug discovery and development.Briefings in bioinformatics,22(6),bbab159Figure from Gaudelet et al.Big Opportunity for Artificial Intelligence|q A massive of data has been generated in th

3、e biomedical domainq Many data are Graph-StructuredGaudelet,T.,Day,B.,Jamasb,A.R.,Soman,J.,Regep,C.,Liu,G.,.&Taylor-King,J.P.(2021).Utilizing graph machine learning within drug discovery and development.Briefings in bioinformatics,22(6),bbab159ChEMBL Dataset(Figure from ChEMBLs homepage.)Illustratio

4、n of a molecular and protein,as well as their graph representation.(Figure from Gaudelet et al.)Big Opportunity for Artificial Intelligence|p A lots of AI techniques have been adopted in Drug Discovery(Drug AI)1 https:/zitniklab.hms.harvard.edu/drugml/Applications of machine learning to drug discove

5、ry and development(Figure from 1)Evaluating Drug AI algorithms|p Several benchmarks have been proposed to bridge the gap between the ML community and real-world drug discovery.p TDC:Therapeutics Data Algorithm Developmentp FS-Mol:few shot learning for moleculesp 1 Stanley,Megan,et al.Fs-mol:A few-sh

6、ot learning dataset of molecules.Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track(Round 2).2021.2 https:/tdcommons.ai/Statistics of FS-Mol(Table from 1)Illustration of TDC dataset(Figure from 2)Evaluating Drug AI algorithms:Issues|p Providing fixed datas