Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction.

ABSTRACT: Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials. Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction. However, traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules. Similarly, graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information. To address these limitations, we propose a novel fingerprint-enhanced hierarchical graph neural network (FH-GNN) for molecular property prediction that simultaneously learns informatio

SUBMITTER: Liu S 

PROVIDER: S-EPMC12246612 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature