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Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches
2022-03
发表期刊SCIENCE CHINA-LIFE SCIENCES (IF:8.0[JCR-2023],7.3[5-Year])
ISSN1674-7305
EISSN1869-1889
DOI10.1007/s11427-021-1946-0
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.
关键词federated learning drug discovery FedAMP Non-IID data
收录类别SCIE
语种英语
WOS研究方向Life Sciences & Biomedicine - Other Topics
WOS类目Biology
WOS记录号WOS:000678456300002
出版者SCIENCE PRESS
原始文献类型Article; Early Access
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/127847
专题生命科学与技术学院_博士生
免疫化学研究所_特聘教授组_蒋华良组
通讯作者Jiang, Hualiang; Qiao, Nan; Zheng, Mingyue
作者单位
1.ShanghaiTech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 200031, Peoples R China;
2.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China;
3.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China;
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
5.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230000, Peoples R China;
6.Huawei Technol Co Ltd, Lab Hlth Intelligence, Shenzhen 518100, Peoples R China
第一作者单位免疫化学研究所;  生命科学与技术学院
通讯作者单位免疫化学研究所;  生命科学与技术学院
第一作者的第一单位免疫化学研究所
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,2022.
APA Xiong, Zhaoping.,Cheng, Ziqiang.,Lin, Xinyuan.,Xu, Chi.,Liu, Xiaohong.,...&Zheng, Mingyue.(2022).Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.SCIENCE CHINA-LIFE SCIENCES.
MLA Xiong, Zhaoping,et al."Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches".SCIENCE CHINA-LIFE SCIENCES (2022).
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