Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors.

ABSTRACT: Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. 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. The proposed platform enhances the inter/extra-pola

SUBMITTER: Guan Y 

PROVIDER: S-EPMC8179287 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature