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Xiong Z, Cheng Z, Lin X, Xu C, Liu X, Wang D, Luo X, Zhang Y, Jiang H, Qiao N, Zheng M. Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches. SCIENCE CHINA-LIFE SCIENCES 2021; 65:529-539. [PMID: 34319533 DOI: 10.1007/s11427-021-1946-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 05/16/2021] [Indexed: 12/11/2022]
Abstract
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.
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Affiliation(s)
- Zhaoping Xiong
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, Shanghai Tech University, Shanghai, 200031, China.,Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ziqiang Cheng
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, Shanghai Tech University, Shanghai, 200031, China.,School of Information Science and Technology, University of Science and Technology of China, Hefei, 230000, China
| | - Xinyuan Lin
- Laboratory of Health Intelligence, Huawei Technologies Co., Ltd, Shenzhen, 518100, China
| | - Chi Xu
- Laboratory of Health Intelligence, Huawei Technologies Co., Ltd, Shenzhen, 518100, China
| | - Xiaohong Liu
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, Shanghai Tech University, Shanghai, 200031, China.,Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Dingyan Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Yong Zhang
- Laboratory of Health Intelligence, Huawei Technologies Co., Ltd, Shenzhen, 518100, China
| | - Hualiang Jiang
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, Shanghai Tech University, Shanghai, 200031, China. .,Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
| | - Nan Qiao
- Laboratory of Health Intelligence, Huawei Technologies Co., Ltd, Shenzhen, 518100, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
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