Predicting the Yield of Pd‐Catalyzed Buchwald–Hartwig Amination Using Machine Learning with Extended Molecular Fingerprints and Selected Physical Parameters

Abstract Machine learning has gained attention due to its ongoing advancements and diverse applications. Within the field of homogeneous catalysis, a prominent area of research in machine learning revolves around predicting reaction yield in Pd‐catalyzed Buchwald–Hartwig amination reactions. This study sought to determine the optimal descriptors for representing the both structural and physical information associated with the reaction, particularly focusing on product details. To achieve this, we assessed the utilization of product extended molecular fingerprints (PEMF) and selected physical parameters (SPP). The utilization of a random forest model incorporating these descriptors yielded promising results in the prediction of reaction yields in Pd‐catalyzed Buchwald–Hartwig amination reactions. The model achieved an impressive R 2 value of 0.943. Data preprocessing on PEMF and sorting preprocessing on physical parameters resulted in a significant reduction in data size to 259 bits PEMF+2 SPPs per prediction, much less than the two previous random forest models which utilized 480 physical parameters and 21,073 bits molecular fingerprints. Although establishing definitive correlations between SPPs and reaction yield presented challenges, our findings indicate that the presence of heavier atoms in the aryl halides may have a beneficial impact within the examined Pd‐catalyzed Buchwald–Hartwig amination reactions, as compared to their analogues.