Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction

Abstract

Reaction selectivity and yield prediction are important for chemical synthesis. Most existing computational methods use either computational expensive and complicated quantum mechanics-based models that are not easy for experimental chemists to use or black-box deep learning models that do not generalize well outside of the training space and lack explanation. Herein, using convenient physics-based electronic descriptors and structure-based steric descriptors, we developed an explainable machine learning platform, Reaxtica, that outperformed previous methods in four different reaction types and tasks, including regioselectivity, site-selectivity, enantioselectivity, and yield predictions. Further descriptor analysis helps understand reaction mechanisms behind the data. As a practical and robust toolbox, Reaxtica can be easily applied to different chemical reactions and extended to out-of-sample reaction. To assist chemists’ daily research, we further built an easy-to-use webserver, which only takes seconds to run and can be accessed at http://www.pkumdl.cn:8000/reaxtica/.