Prediction of chemical reaction yields with large-scale multi-view pre-training.

ABSTRACT: Developing machine learning models with high generalization capability for predicting chemical reaction yields is of significant interest and importance. The efficacy of such models depends heavily on the representation of chemical reactions, which has commonly been learned from SMILES or graphs of molecules using deep neural networks. However, the progression of chemical reactions is inherently determined by the molecular 3D geometric properties, which have been recently highlighted as crucial features in accurately predicting molecular properties and chemical reactions. Additionally, large-scale pre-training has been shown to be essential in enhancing the generalization capability of complex deep learning models. Based on these considerations, we propose the Reaction Multi-View Pre-train

SUBMITTER: Shi R 

PROVIDER: S-EPMC10895839 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature