A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

Abstract

Development of suitable machine-learning (ML) approaches by using molecular descriptors can provide significant impetus to current efforts in asymmetric catalysis, wherein one strives to make a desired stereoisomer of a given handedness (enantiomer) in a highly selective manner. The proposed approach provides a sustainable model that trains on known catalysts and helps to predict the efficacy of additional catalysts for asymmetric synthesis, thereby expediting the discovery with lesser cost as compared to traditional empirical methods. Training ML algorithms first using the available known examples—predicting additional catalysts using such algorithms for subsequent in-line experimental validation, re-training by augmenting with the additional data thus generated—can provide a superior train–predict–train cycle suitable for accelerated reaction discovery.