Machine learning induction of chemically intuitive rules for the prediction of enantioselectivity in the asymmetric syntheses of alcohols

In an asymmetric catalytic reaction involving a specific catalyst, the prediction/determination of the absolute configuration of the major product is one of the essential issues. Some encouraging results of enantioselectivity prediction in asymmetric reactions have been achieved by chemoinformatics methods. However, intuitive empirical rules of enantioselectivity are to be preferred by chemists. This investigation attempted to combine chemoinformatics methods with the empirical rules of experts, i.e., to build chemoinformatics models on the basis of intuitive descriptors to derive interpretable empirical rules. In order to implement this, chiral substituent codes were specially developed, and Fisher linear discriminant analysis was used to construct the relationship between chiral substituent codes and the absolute configurations of the products in asymmetric reactions. The method was successfully applied to two data sets of reactions, namely to the products of the reaction – chiral alcohols. On the basis of the excellent results of structure–enantioselectivity relationships (SER), several empirical rules were extracted from the constructed mathematical models, which highlighted aspects of the mechanism and can almost entirely replace the analytical expressions. This investigation extends the application of chemoinfomatics methods to the area of generation of empirical rules, and the idea has the potential to be generally applied with enantioselective reactions by using different codes and different machine learning methods.