| |||||||
ShanghaiTech University Knowledge Management System
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]) |
ISSN | 1674-7305 |
EISSN | 1869-1889 |
DOI | 10.1007/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 |
引用统计 | 正在获取...
|
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). |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 |
修改评论
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。