Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors

Abstract

Machine learning can predict the major regio‐, site‐, and diastereoselective outcomes of Diels–Alder reactions better than standard quantum‐mechanical methods and with accuracies exceeding 90 % provided that i) the diene/dienophile substrates are represented by “physical‐organic” descriptors reflecting the electronic and steric characteristics of their substituents and ii) the positions of such substituents relative to the reaction core are encoded (“vectorized”) in an informative way.