CMMSCL_DPI

CMMSCL-DPI: Cross-Modal Multi-Structural Contrastive Learning for Predicting Drug-Protein Interactions

AbstractPredicting drug-protein interactions (DPI) is essential for effective and safe drug discovery. Although deep learning methods have been extensively applied to DPI prediction, effectively leveraging the multi-structural and multi-modal data of drugs and proteins to enhance prediction accuracy remains a significant challenge. This study proposed CMMSCL-DPI, a cross-modal multi-structural contrastive learning model. CMMSCL-DPI applies contrastive learning to the multi-dimensional structural features of proteins and drugs separately and integrates interaction features from a DPI heterogeneous graph network to facilitate cross-modal contrastive learning. This approach effectively captures the key differences and similarities between proteins and drugs, significantly enhancing the model's generalization capabilities for novel drug-target pairs. Experimental results across four benchmark datasets demonstrate that CMMSCL-DPI outperforms five state-of-the-art baseline models in overall performance. Additionally, the model successfully identified an unreported drug-protein interaction, which was subsequently validated through all-atom molecular dynamics simulations. This case study not only confirms the predictive accuracy of CMMSCL-DPI but also underscores its potential in discovering novel protein-ligand interactions.

Data and Code Availability
All data and code supporting this study are publicly available on Zenodo:
https://zenodo.org/records/18326108  DOI: 10.5281/zenodo.18326108

Keywords:
Drug–Protein Interaction (DPI); Contrastive Learning; Deep Learning; Graph Neural Networks; Drug Discovery; Multimodal Representation; Multi-Modal Fusion;