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FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery.

Authors
  • Chen, Shaoqi1
  • Xue, Dongyu1
  • Chuai, Guohui1
  • Yang, Qiang2, 3
  • Liu, Qi1
  • 1 Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China. , (China)
  • 2 Department of AI, WeBank, Shenzhen, China. , (China)
  • 3 Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong. , (Hong Kong SAR China)
Type
Published Article
Journal
Bioinformatics (Oxford, England)
Publication Date
Dec 08, 2020
Identifiers
DOI: 10.1093/bioinformatics/btaa1006
PMID: 33289524
Source
Medline
Language
English
License
Unknown

Abstract

Quantitative structure-activity relationship (QSAR) analysis is commonly used in drug discovery. Collaborations among pharmaceutical institutions can lead to a better performance in QSAR prediction, however, intellectual property and related financial interests remain substantially hindering inter-institutional collaborations in QSAR modeling for drug discovery. For the first time, we verified the feasibility of applying the horizontal federated learning (HFL), which is a recently developed collaborative and privacy-preserving learning framework to perform QSAR analysis. A prototype platform of federated-learning-based QSAR modeling for collaborative drug discovery, i.e, FL-QSAR, is presented accordingly. We first compared the HFL framework with a classic privacy-preserving computation framework, i.e., secure multiparty computation (MPC) to indicate its difference from various perspective. Then we compared FL-QSAR with the public collaboration in terms of QSAR modeling. Our extensive experiments demonstrated that (1) collaboration by FL-QSAR outperforms a single client using only its private data, and (2) collaboration by FL-QSAR achieves almost the same performance as that of collaboration via cleartext learning algorithms using all shared information. Taking together, our results indicate that FL-QSAR under the HFL framework provides an efficient solution to break the barriers between pharmaceutical institutions in QSAR modeling, therefore promote the development of collaborative and privacy-preserving drug discovery with extendable ability to other privacy-related biomedical areas. The source codes of FL-QSAR are available on the GitHub: https://github.com/bm2-lab/FL-QSAR. Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]

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