{"title":"用联合学习打破药物发现中的数据孤岛","authors":"Can Li","doi":"10.1038/s44286-025-00213-x","DOIUrl":null,"url":null,"abstract":"Federated learning enables collaboration without compromising data privacy, but challenges remain. Now, a data-centric approach named FLuID enhances knowledge sharing in drug discovery, demonstrating improved predictive performance while preserving confidentiality across pharmaceutical companies.","PeriodicalId":501699,"journal":{"name":"Nature Chemical Engineering","volume":"2 5","pages":"288-289"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Breaking data silos in drug discovery with federated learning\",\"authors\":\"Can Li\",\"doi\":\"10.1038/s44286-025-00213-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Federated learning enables collaboration without compromising data privacy, but challenges remain. Now, a data-centric approach named FLuID enhances knowledge sharing in drug discovery, demonstrating improved predictive performance while preserving confidentiality across pharmaceutical companies.\",\"PeriodicalId\":501699,\"journal\":{\"name\":\"Nature Chemical Engineering\",\"volume\":\"2 5\",\"pages\":\"288-289\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Chemical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s44286-025-00213-x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Chemical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44286-025-00213-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Breaking data silos in drug discovery with federated learning
Federated learning enables collaboration without compromising data privacy, but challenges remain. Now, a data-centric approach named FLuID enhances knowledge sharing in drug discovery, demonstrating improved predictive performance while preserving confidentiality across pharmaceutical companies.
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