These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
MLQM_selectivity_preprint_manuscript.pdf (2.44 MB)

Regio-Selectivity Prediction with a Machine-Learned Reaction Representation and On-the-Fly Quantum Mechanical Descriptors

submitted on 02.09.2020, 15:53 and posted on 03.09.2020, 12:30 by Yanfei Guan, Connor Coley, Haoyang wu, Duminda Ranasinghe, esther heid, Thomas J. Struble, Lagnajit Pattanaik, William H. Green, Klavs F. Jensen

We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on ab initio calculations of 130k organic molecules, and train a multi-task constrained model to calculate demanded descriptors on-the-fly.


Email Address of Submitting Author


Massachusetts Institute of Technology


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

There are no conflicts to declare