Dual-Attention Multimodal Framework for Molecular Property Prediction.

Self-Supervised Learning for Molecular Property Prediction: Methods, Multimodal Insights, and Benchmark Comparisons.

This review provides a systematic overview of recent advances in SSL-based molecular property prediction and analyzes how multimodal molecular representation learning by integrating sequence, graph, three-dimensional structure, and textual information can improve the quality and expressiveness of molecular representations.

Attention based multi-scale fusion graphical framework for molecular docking of quinoline carboxamide analogues

Multimodal feature fusion for molecular property classification.

This study provides a large-scale empirical evaluation of multimodal feature fusion for molecular property classification by systematically integrating SMILES-based chemical language representations with fingerprint-based structural descriptors across 60 benchmark datasets.

MSMPP: Molecular Property Prediction by Integrating Multi-scale Multi-view information with pretrained 3D molecular large model representation.

Evaluations on eight MoleculeNet datasets show that MSMPP significantly outperforms state-of-the-art models, demonstrating its effectiveness in integrating multi-view intra-molecular features, inter-molecular features and cross-task information.

Dual representation learning-based drug synergy prediction via sequence and molecular network reconstruction

The DRL-DSP is proposed, a novel dual representation learning framework designed to enhance drug synergy prediction by integrating molecular-level features from SMILES sequences with graph-level relational information from reconstructed molecular networks.

HyperMolFusion: A Hypergraph-Enhanced Multi-Modal Fusion Framework for Accurate Molecular Property Prediction.

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.