Atom-Based Machine Learning for Estimating Nucleophilicity and Electrophilicity with Applications to Retrosynthesis and Chemical Stability

Abstract

Nucleophilicity and electrophilicity are important properties for evaluating the reac- tivity and selectivity of chemical reactions. It allows the ranking of nucleophiles and electrophiles on reactivity scales, enabling a better understanding and prediction of re- action outcomes. Building upon our recent work (Digit. Discov., 2024, 3, 347-354), we introduce an atom-based machine learning (ML) approach for predicting methyl cation affinities (MCAs) and methyl anion affinities (MAAs) to estimate nucleophilicity and electrophilicity, respectively. The ML models are trained and validated on QM-derived data from around 50,000 neutral drug-like molecules, achieving Pearson correlation co- efficients of 0.97 for MCA and 0.95 for MAA on the held-out test sets. In addition, we demonstrate the ML approach on two different applications: first, as a general tool for filtering retrosynthetic routes based on chemical selectivity predictions, and second, as a tool for assessing the chemical stability of esters and carbamates towards hydrolysis reactions. The code is freely available on GitHub under the MIT open source license and as a web application at www.esnuel.org.