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人工智能药物研发的数据问题。

AI drug development's data problem.

作者信息

Gold E Richard, Cook-Deegan Robert

机构信息

E. Richard Gold is at the Faculty of Law and Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; is Chief Policy and Partnerships Officer, Conscience, Toronto, ON, Canada; and is senior fellow, Centre for International Governance Innovation, Waterloo, ON, Canada.

Robert Cook-Deegan is at the School for the Future of Innovation in Society and Consortium for Science, Policy and Outcomes, Arizona State University, Washington, DC, USA.

出版信息

Science. 2025 Apr 11;388(6743):131. doi: 10.1126/science.adx0339. Epub 2025 Apr 10.

DOI:10.1126/science.adx0339
PMID:40208973
Abstract

The future of drug discovery may be artificial intelligence (AI), but its present is not. AI is in its infancy in the field. To help AI mature, developers need nonproprietary, open, large, high-quality datasets to train and validate models, managed by independent organizations.

摘要

药物研发的未来或许是人工智能(AI),但其当下并非如此。人工智能在该领域尚处于起步阶段。为助力人工智能走向成熟,开发者需要由独立机构管理的非专有、开放、大型且高质量的数据集,用于训练和验证模型。

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