The application of computational methods in enantioselective catalysis has evolved from the rationalization of the observed stereochemical outcome to their prediction and application to the design of chiral ligands. This Perspective provides an overview of the current methods used, ranging from atomistic modeling of the transition structures involved to correlation-based methods with particular emphasis placed on the Q2MM/CatVS method. Using three enantioselective palladium-catalyzed reactions, namely, the conjugate addition of arylboronic acids to enones, the enantioselective redox relay Heck reaction, and the Tsuji–Trost allylic amination as case studies, we argue that computational methods have become truly equal partners to experimental studies in that, in some cases, they are able to correct published stereochemical assignments. Finally, the consequences of this approach to data-driven methods are discussed. 

Interplay of Computation and Experiment in Enantioselective Catalysis: Rationalization, Prediction, and─Correction?

Stereoselective reactions (both chemical and enzymatic reactions) have been essential for origin of life, evolution, human biology and medicine. Since late 1960s, there have been numerous successes in the exciting new frontier of asymmetric catalysis. However, most industrial and academic asymmetric catalysis nowadays do follow the trial-and-error model, since the energetic difference for success or failure in asymmetric catalysis is incredibly small. Our current understanding about stereoselective reactions is mostly qualitative that stereoselectivity arises from differences in steric effects and electronic effects in multiple competing mechanistic pathways. Quantitatively understanding and modulating the stereoselectivity of for a given chemical reaction still remains extremely difficult. As a proof of principle, we herein present a novel machine learning technique, which combines a LASSO model and two Random Forest model via two Gaussian Mixture models, for quantitatively predicting stereoselectivity of chemical reactions. Compared to the recent ground-breaking approach [1], our approach is able to capture interactions between features and exploit complex data distributions, which are important for predicting stereoselectivity. Experimental results on a recently published dataset demonstrate that our approach significantly outperform [1]. The insight obtained from our results provide a solid foundation for further exploration of other synthetically valuable yet mechanistically intriguing stereoselective reactions.

Predicting the Stereoselectivity of Chemical Transformations by Machine Learning

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Predicting the Stereoselectivity of Chemical Transformations by Machine Learning