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Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches

文献类型：期刊论文

作者Xiong, Zhaoping2,3,4,5; Cheng, Ziqiang2,3,6; Lin, Xinyuan1; Xu, Chi1; Liu, Xiaohong2,3,4; Wang, Dingyan4,5; Luo, Xiaomin4; Zhang, Yong1; Jiang, Hualiang2,3,4; Qiao, Nan1
刊名SCIENCE CHINA-LIFE SCIENCES
出版日期2021-07-26
页码11
关键词federated learning drug discovery FedAMP Non-IID data
ISSN号1674-7305
DOI10.1007/s11427-021-1946-0
通讯作者Jiang, Hualiang(hljiang@simm.ac.cn) ; Qiao, Nan(qiaonan3@huawei.com) ; Zheng, Mingyue(myzheng@simm.ac.cn)
英文摘要Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of the data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success rate of AI-powered drug discovery. Here, we simulated the federated learning process with different property and activity datasets from different sources, among which overlapping molecules with high or low biases exist in the recorded values. Beyond the benefit of gaining more data, we also demonstrated that federated training has a regularization effect superior to centralized training on the pooled datasets with high biases. Moreover, different network architectures for clients and aggregation algorithms for coordinators have been compared on the performance of federated learning, where personalized federated learning shows promising results. Our work demonstrates the applicability of federated learning in predicting drug-related properties and highlights its promising role in addressing the small and biased data dilemma in drug discovery.
WOS关键词AQUEOUS SOLUBILITY ; HERG ; PREDICTION ; MODEL ; CLASSIFICATION ; RESISTANCE ; INHIBITORS ; MECHANISM ; COMPOUND ; SECURE
资助项目Shanghai Municipal Science and Technology Major Project ; National Natural Science Foundation of China[81773634] ; National Science and Technology Major Project of the Ministry of Science and Technology of China[2018ZX09711002] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12050201] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12020368]
WOS研究方向Life Sciences & Biomedicine - Other Topics
语种英语
WOS记录号WOS:000678456300002
出版者SCIENCE PRESS
源URL[http://119.78.100.183/handle/2S10ELR8/298174]  
专题新药研究国家重点实验室
通讯作者Jiang, Hualiang; Qiao, Nan; Zheng, Mingyue
作者单位1.Huawei Technol Co Ltd, Lab Hlth Intelligence, Shenzhen 518100, Peoples R China
2.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 200031, Peoples R China
3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China
4.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
6.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230000, Peoples R China
推荐引用方式
GB/T 7714		 Xiong, Zhaoping,Cheng, Ziqiang,Lin, Xinyuan,et al. Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches[J]. SCIENCE CHINA-LIFE SCIENCES,2021:11.
APA Xiong, Zhaoping.,Cheng, Ziqiang.,Lin, Xinyuan.,Xu, Chi.,Liu, Xiaohong.,...&Zheng, Mingyue.(2021).Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.SCIENCE CHINA-LIFE SCIENCES,11.
MLA Xiong, Zhaoping,et al."Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches".SCIENCE CHINA-LIFE SCIENCES (2021):11.

入库方式： OAI收割

来源：上海药物研究所

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