Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction

Abstract

Advancements in synthetic organic chemistry are closely related to understanding substrate and catalyst reactivities through detailed mechanistic studies. Traditional mechanistic investigations are labor-intensive and rely on experimental kinetic, thermodynamic, and spectroscopic data. Linear free energy relationships (LFERs), exemplified by Hammett relationships, have long facilitated reactivity prediction despite their inherent limitations when using experimental constants or incorporating comprehensive experimental data. Data-driven modeling, which integrates cheminformatics with machine learning, offers powerful tools for predicting and interpreting mechanisms and effectively handling complex reactivities through multiparameter strategies. This review explores selected examples of data-driven strategies for investigating organic reaction mechanisms. It highlights the evolution and application of computational descriptors for mechanistic inference.