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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 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 (i) collaboration by FL-QSAR outperforms a single client using only its private data, and (ii) 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source codes of FL-QSAR are available on the GitHub: https:\/\/github.com\/bm2-lab\/FL-QSAR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa1006","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T16:36:22Z","timestamp":1605803782000},"page":"5492-5498","source":"Crossref","is-referenced-by-count":74,"title":["FL-QSAR: a federated learning-based QSAR prototype for collaborative drug discovery"],"prefix":"10.1093","volume":"36","author":[{"given":"Shaoqi","family":"Chen","sequence":"first","affiliation":[{"name":"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 200092, China"}]},{"given":"Dongyu","family":"Xue","sequence":"additional","affiliation":[{"name":"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 200092, China"}]},{"given":"Guohui","family":"Chuai","sequence":"additional","affiliation":[{"name":"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 200092, China"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of AI, WeBank , Shenzhen 518055, China"},{"name":"Department of Computer Science and Engineering, Hong Kong University of Science and Technology , Clear Water Bay, Hong Kong, China"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"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 200092, China"}]}],"member":"286","published-online":{"date-parts":[[2020,12,8]]},"reference":[{"key":"2023062707103976000_btaa1006-B2","first-page":"1","author":"Ben-Or","year":"1988"},{"key":"2023062707103976000_btaa1006-B3","first-page":"192","volume-title":"Sharemind: A Framework for Fast Privacy-Preserving Computations","author":"Bogdanov","year":"2008"},{"key":"2023062707103976000_btaa1006-B4","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/nbt.4108","article-title":"Secure genome-wide association analysis using multiparty computation","volume":"36","author":"Cho","year":"2018","journal-title":"Nat. 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