Catalyzing Change: The Power of Computational Asymmetric Catalysis

Computational asymmetric catalysis has seen an impressive rise in the last twenty years, thanks to advancements in algorithm and method development for predicting catalyst enantioselectivity. These methods/algorithms describe reactions that can be categorized into two groups: reactions where 1) knowledge of the mechanism is not required and where leveraging experimental data to establish correlations between reaction descriptors and enantioselectivity is imperative, and 2) the mechanism (or transition state (TS) for the enantioselective step) is known and used to determine catalyst stereoselectivity by modeling the diastereomeric TSs. Although these methods have reached an important level of proficiency for enantioselectivity prediction, this field remains largely obscured for experimental chemists. In this review, we aim to shed light on models, methods, and applications used in asymmetric synthesis, with accessible language suited for experimental chemists. Our hope is that these methods will ultimately be adopted by synthetic chemists for the design of novel catalysts.