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AI drug development's data problem
E Richard Gold1, Robert Cook-Deegan2
1E. 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.
Science (New York, N.Y.)
|April 10, 2025
Summary
Artificial intelligence (AI) holds promise for drug discovery but requires significant development. Open, high-quality datasets managed by independent organizations are crucial for training and validating AI models to advance the field.
Area of Science:
- Computational chemistry
- Bioinformatics
- Artificial intelligence in medicine
Background:
- Artificial intelligence (AI) is poised to revolutionize drug discovery.
- However, AI applications in this field are currently in their nascent stages.
Discussion:
- The advancement of AI in drug discovery is contingent upon the availability of comprehensive, high-quality datasets.
- Proprietary data formats and limited access hinder the development and validation of AI models.
Key Insights:
- Independent organizations should manage large, open-access datasets.
- These datasets are essential for training and validating AI algorithms effectively.
- Standardization and accessibility of data are paramount for AI maturation in drug discovery.
Outlook:
- Facilitating open data initiatives will accelerate AI's integration into pharmaceutical research.
- Collaborative efforts are needed to build robust, generalizable AI models for drug development.
- The future of AI in drug discovery depends on addressing current data limitations.
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