Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Abstract

Predicting catalyst selectivity Asymmetric catalysis is widely used in chemical research and manufacturing to access just one of two possible mirror-image products. Nonetheless, the process of tuning catalyst structure to optimize selectivity is still largely empirical. Zahrt et al. present a framework for more efficient, predictive optimization. As a proof of principle, they focused on a known coupling reaction of imines and thiols catalyzed by chiral phosphoric acid compounds. By modeling multiple conformations of more than 800 prospective catalysts, and then training machine-learning algorithms on a subset of experimental results, they achieved highly accurate predictions of enantioselectivities. Science , this issue p. eaau5631