Highly accurate prediction of reaction performance and enantioselectivity with a uniform machine learning protocol

Abstract

Synthetic reactions, especially asymmetric reactions are key components of modern chemistry. Chemists have put enormous experimental effort into recognizing various molecule patterns to enable efficient synthesis and asymmetric catalysis. Recent application of machine learning algorithms and chemoinformatics in this field demonstrated their huge potential in facilitating this process by accurate prediction. However, existing methods are relatively limited to specific designed data set, and only implement single prediction of reaction performance or reaction enantioselectivity, rendering their general use in broader scenarios challenging. Here we provide a uniform machine learning protocol that can predict both reaction performance and enantioselectivity with high accuracy. Reconstruction of molecular chemical space derived from more comprehensive three-dimensional atomic and molecular descriptors allow for training of our neural network-based model over four representative datasets. This uniform machine learning protocol was validated with outperformance of accuracy than other methods over all four cases (C-C, C-N, C-S cross coupling reactions and asymmetric hydrogenation) in the prediction of both reaction performance and enantioselectivity. It was also successfully applied to the out-of-set and sparse set prediction, leveraging its possible wide application in accelerating synthesis improve and molecular architects.