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/.
Supporting Information for Reaxtica
Supporting Information for "Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction"