HyperMolFusion: A Hypergraph-Enhanced Multi-Modal Fusion Framework for Accurate Molecular Property Prediction.
TL;DR
By holistically integrating atomic, motif, and global fingerprint information via hypergraph modeling, HyperMolFusion offers a more reliable computational tool to enhance the efficiency and accuracy of drug development pipelines.
Abstract
Molecular property prediction is a critical task in accelerating drug discovery. While deep learning has shown promise, prevailing single-modal methods struggle to integrate multi-source (e.g., atomic graph and molecular fingerprints), heterogeneous chemical knowledge, thereby failing to holistically represent molecular structures and capture the high-order synergistic interactions governing their functions. To address these challenges, we present HyperMolFusion, a hypergraph-enhanced multi-modal fusion model for molecular property prediction. Compared with traditional graphs limited to pairwise atomic bonds, HyperMolFusion models chemical motifs as hyperedges to explicitly capture high-order structural correlations and encode complex molecular interactions. The framework comprises three core representation learning modules: AtomConv for local atomic interaction learning via attention-enhanced message passing, HyperConv for motif-level high-order correlation extraction via hypergraph convolution with GRU gating, and a mixed molecular fingerprint module that adaptively integrates MACCS, PubChem, and Pharmacophore fingerprints. A chemically guided attention (CGA) mechanism then dynamically fuses these multi-level features into hierarchical molecular representations, alleviating over-smoothing and preserving structural information effectively. Evaluated on eight MoleculeNet benchmarks (covering regression and classification tasks), HyperMolFusion achieves promising performance. For regression, it achieves an RMSE of 0.611 in lipophilicity, 0.653 in ESOL, and 0.951 in FreeSolv. For classification, it achieves a ROC-AUC of 0.935 in ClinTox, 0.907 in BBBP, and 0.689 in SIDER. This work provides a systematic and effective solution for molecular property prediction: by holistically integrating atomic, motif, and global fingerprint information via hypergraph modeling, HyperMolFusion offers a more reliable computational tool to enhance the efficiency and accuracy of drug development pipelines.