DeepReac+: deep active learning for quantitative modeling of organic chemical reactions.

ABSTRACT: Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demon

SUBMITTER: Gong Y 

PROVIDER: S-EPMC8580052 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature