Enhanced Regioselectivity Prediction of sp2 C-H Halogenation via Negative Data Augmentation and Multimodel Integration.

Abstract

Efficient molecular editing is pivotal in synthetic chemistry, especially for developing drugs, materials, and high-value chemicals. Electrophilic aromatic substitution (S_EAr) reactions, specifically sp² C-H halogenation, face significant challenges due to electronic and steric factors, necessitating extensive trial-and-error. This study introduces an innovative machine learning-based model to predict halogenation sites in S_EAr reactions, achieving an average accuracy of 93% in 5-fold cross-validation. Employing ensemble techniques, particularly AutoGluon-Tabular (AG), the model demonstrates broad applicability across various aromatic halides, enhancing its utility in drug design, materials science, and more. By reducing experimental uncertainty and optimizing synthetic pathways, this model saves considerable time and resources, thereby accelerating innovation in synthetic chemistry.