AI drug development's data problem.

Journal: Science (New York, N.Y.)
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.

Authors

  • E Richard Gold
    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
    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.

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