ProtoMol: enhancing molecular property prediction via prototype-guided multimodal learning.

ABSTRACT: Multimodal molecular representation learning, which jointly models molecular graphs and their textual descriptions, enhances predictive accuracy and interpretability by enabling more robust and reliable predictions of drug toxicity, bioactivity, and physicochemical properties through the integration of structural and semantic information. However, existing multimodal methods suffer from two key limitations: (i) they typically perform cross-modal interaction only at the final encoder layer, thus overlooking hierarchical semantic dependencies; (ii) they lack a unified prototype space for robust alignment between modalities. To address these limitations, we propose ProtoMol, a prototype-guided multimodal framework that enables fine-grained integration and consistent semantic alignment between

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12684735 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature