Affiliation:
1. Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
Abstract
Machine learning has revolutionized information processing for large datasets across various fields. However, its limited interpretability poses a significant challenge when applied to chemistry. In this study, we developed a set of simple molecular representations to capture the structural information of ligands in palladium-catalyzed Sonogashira coupling reactions of aryl bromides. Drawing inspiration from human understanding of catalytic cycles, we used a graph neural network to extract structural details of the phosphine ligand, a major contributor to the overall activation energy. We combined these simple molecular representations with an electronic descriptor of aryl bromide as inputs for a fully connected neural network unit. The results allowed us to predict rate constants and gain mechanistic insights into the rate-limiting oxidative addition process using a relatively small dataset. This study highlights the importance of incorporating domain knowledge in machine learning and presents an alternative approach to data analysis.
Funder
Research Grants Council of Hong Kong
Subject
Chemistry (miscellaneous),Analytical Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Molecular Medicine,Drug Discovery,Pharmaceutical Science