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The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET).
Rapid advances in AI technology have made these considerations critical across the industry, with public and private stakeholders rushing to catch up, as evidenced by guiding principles for ML-enabled devices recently issued by FDA Center for Devices and Radiological Health (CDRH).
It’s time to put that technology to work widely — in ways that prioritize conscientious protocols designed to prevent bias in data gathering and use in patient care, said Nigam Shah, MBBS, PhD, a Stanford Medicine professor of biomedical data science and of medicine.