Prediction of enantioselectivity in thiol addition to imines catalyzed by chiral phosphoric acids

Abstract

Predicting enantioselectivities in asymmetric catalytic reactions through chemometric approach is a challenging area. In this paper, quantitative structure−selectivity relationship (QSSR) models were successfully developed for thiol addition to N‐acylimines catalyzed by chiral phosphoric acids. Ten Dragon molecular descriptors calculated from thiols and phosphoric acid catalysts were used to correlate with 1075 enantioselectivities ΔΔG. Machine learning algorithms, support vector machine (SVM) and random forest (RF), were adopted to build QSSRs after descriptor subset selection based on the multiple linear regression technique. The SVM and RF models, respectively, have coefficient of determination R2 of 0.925 and 0.944 for the training set, and of 0.837 and 0.851 for the test set. Both SVM and RF models were subjected to internal and external validation and estimation in prediction ability and systematic error.

Predicting the stereoselectivity of chemical reactions by composite machine learning method

“…Moon et al 5 developed a machine learning model by using the Random Forest (RF) algorithm to predict the stereoselectivity in glycosylation reactions. Yu 6 developed predictive models using Support Vector Machine (SVM) and RF algorithms to predict enantioselectivities in asymmetric catalytic reactions with a particular focus on thiol addition to N-acylimines catalyzed by chiral phosphoric acids. These models outperform traditional linear regression methods, which indicate the effectiveness of nonlinear machine learning algorithms in predicting enantioselectivities.…”