Data-driven multi-objective optimization tactics for catalytic asymmetric reactions

Optimization of catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantio-, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >500 bisphosphine ligands was constructed and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs suggesting a general strategy for the optimization of reactions where performance is controlled by bisphosphine ligands.