MvMRL: a multi-view molecular representation learning method for molecular property prediction.

ABSTRACT: Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previous molecular representation learning studies often suffer from limitations, such as over-reliance on a single molecular representation, failure to fully capture both local and global information in molecular structure, and ineffective integration of multiscale features from different molecular representations. These limitations restrict the complete and accurate representation of molecular structure and properties, ultimately impacting the accuracy of predicting molecular properties. To this end, we propose a novel multi-view molecular represent

SUBMITTER: Zhang R 

PROVIDER: S-EPMC11200189 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature