Yale researchers unveil AI platform for faster chemistry discovery
The platform has already enabled the successful synthesis of dozens of previously unreported compounds across diverse chemical fields.
Researchers at Yale University have developed an AI platform that accelerates chemical discovery by turning scientific knowledge into practical laboratory guidance. The system, known as MOSAIC, generates detailed experimental procedures across chemistry, including drug design and materials science.
MOSAIC differs from existing AI chemistry tools by combining thousands of specialised AI ‘experts,’ each representing a distinct area of chemical knowledge.
Instead of a single model, the platform draws on diverse reaction expertise to guide complex syntheses, including the synthesis of previously unreported compounds.
Early results suggest the approach significantly improves experimental outcomes. Using MOSAIC, researchers successfully synthesised more than 35 new compounds, spanning pharmaceuticals, catalysts, advanced materials, and other chemical domains.
The system also provides uncertainty estimates, helping scientists prioritise experiments most likely to succeed.
Designed as an open-source framework, MOSAIC aims to move AI beyond prediction and into hands-on laboratory support. Developers say the platform could cut research bottlenecks, improve reproducibility, and widen access to advanced chemical synthesis.
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