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

03 September 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Reactivity Prediction
reaction modeling
Reactivity Descriptors
feature learning
Feature Engineering


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