Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view.

ABSTRACT: Molecular representation learning (MRL) has shown promise in accelerating drug development by predicting chemical properties. However, imperfectly annotation among datasets pose challenges in model design and explainability. In this work, we formulate molecules and corresponding properties as a hypergraph, extracting three key relationships: among properties, molecule-to-property, and among molecules, and developed a unified and explainable multi-task MRL framework, OmniMol. It integrates a task-related meta-information encoder and a task-routed mixture of experts (t-MoE) backbone to capture correlations among properties and produce task-adaptive outputs. To capture underlying physical principles among molecules, we implement an innovative SE(3)-encoder for physical symmetry, applying equi

SUBMITTER: Wang B 

PROVIDER: S-EPMC12484882 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature