CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug-drug interaction prediction.

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.

IAMV-DDI: Interaction-Aware Multi-View Molecular Representation Learning for Drug-Drug Interaction Event Prediction.

IAMV-DDI is proposed, an interaction-aware multi-view molecular representation learning framework for DDI prediction and DDI event classification that achieves strong performance compared with representative network-based, chemical-structure-based, and hybrid baseline methods.

MUIGSL: Molecular Uncertainty-Aware Iterative Graph Structure Learning for Molecular Property Prediction

Molecular Property Prediction (MPP) is a core task in computer-aided drug discovery. Traditional methods primarily focus on intramolecular information propagation while neglecting intermolecular relationships. While recent graph structure learning methods attempt to construct intermolecular graphs, they often ignore di...

MSGRL: A Motif-Driven Self-Supervised Graph Representation Learning Framework for Interpretable Molecular Property Prediction

Molecular property prediction is a fundamental task in drug discovery and chemical biology, where effective molecular representations are essential for accurate prediction. Learning transferable motif-level representations remains challenging because explicit motif annotations are scarce and existing representations ma...

PACL: property-aware contrastive learning with adaptive substructures for molecular property prediction

Dual-Attention Multimodal Framework for Molecular Property Prediction.

A novel Dual-Attention Multimodal framework for Graphs and Sequence-based representations, so-called DAM-GS, which provides a promising solution for molecular property prediction with broad applications in drug discovery and computational molecular science.