Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.

ABSTRACT: The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust, and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction and thus an ideal input for graph-based machine learning approaches. The model learns to create a da

SUBMITTER: Heid E 

PROVIDER: S-EPMC9092344 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature