RT Journal Article SR Electronic T1 Facing small and biased data dilemma in drug discovery with federated learning JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.03.19.998898 DO 10.1101/2020.03.19.998898 A1 Zhaoping Xiong A1 Ziqiang Cheng A1 Xiaohong Liu A1 Dingyan Wang A1 Xiaomin Luo A1 Mingyue Zheng A1 Hualiang Jiang YR 2020 UL http://biorxiv.org/content/early/2020/03/20/2020.03.19.998898.1.abstract AB 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 these data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success of AI-powered drug discovery. We here simulate the federated learning process with 7 aqueous solubility datasets from different sources, among which there are overlapping molecules with high or low biases in the recorded values. Beyond the benefit of gaining more data, we also demonstrate federated training has a regularization effect making it superior than centralized training on the pooled datasets with high biases. Further, federated model customization for each client can effectively help us deal with the highly biased data in drug discovery and achieve better generalization performance. Our work demonstrates the application of federated learning in predicting drug related properties, but also highlights its promising role in addressing the small data and biased data dilemma in drug discovery.