DeepGCL: Multi-View Graph Contrastive Learning for Enhanced Drug-Target Binding Affinity Prediction Through Protein...

Drug-target binding affinity (DTA) prediction plays a critical role in drug discovery. Although recent approaches have demonstrated the importance of incorporating protein pocket information, they still struggle to jointly model local atomic-level interactions and global sequence contexts, as well as capture their intricate cross-modal dependencies, within a unified framework for accurate prediction. To address this limitation, we present DeepGCL, a novel multi-modal framework that leverages multi-view graph contrastive learning to capture latent representations of pocket-drug interactions and their underlying molecular determinants. The DeepGCL integrates protein sequences, pocket-drug interaction graphs, and drug molecular graphs within a unified framework to learn complementary molecular representations. By combining sequence encoding with multi-view graph contrastive learning through augmented view generation, the DeepGCL effectively captures contextual protein information and structural interactions between pockets and drugs. This enables accurate characterization of spatial and chemical relationships between binding partners. Comprehensive evaluation on multiple benchmarks shows that DeepGCL consistently outperforms state-of-the-art methods across standard metrics. Ablation studies further confirm that the protein feature module and graph contrastive learning module both make significant and complementary contributions to performance. These findings underscore the effectiveness of multi-view learning paradigms in capturing the multifaceted nature of drug-target interactions and highlight the importance of integrating diverse molecular representations for accurate binding affinity prediction. Our code is available at https://github.com/domtryto/DeepGCL.