Foundation model for chemistry — predicting reaction conditions and process parameters that would take months to find through traditional lab work.

Chemistry optimization is the rate-limiting step in drug development and chemical manufacturing — finding the right reaction conditions for a new compound requires months of iterative lab work, each failed experiment consuming time and resources. Traditional process development runs on trial and error, and the search space of variables — temperature, catalyst, concentration, solvent — is too large to explore exhaustively. Yoneda's AI predicts optimal reaction conditions from experimental data, compressing months of bench work to days.

Foundation models trained on experimental reaction data have reached the accuracy threshold where predicted conditions translate reliably to lab outcomes — making AI-assisted chemistry a practical tool, not a research project. The same inflection that made AlphaFold transformative for protein structure is now happening for reaction chemistry.