CoPocket: Pocket-Conditioned Molecular Generation via Multi-Modal Feature Constraints and Interaction-Aware Contrastive Learning

Targeted molecular generation aims to generate molecules against protein pockets, which is a challenging task in drug discovery. Most existing methods neglect the multiscale hierarchical representation of protein pockets, and fail to sufficiently exploit critical interaction within protein-ligand binding. This paper proposes a drug design framework, namely CoPocket, focusing on different modal features of binding pockets to guide high quality molecule generation. First, we construct an atomic-level spatial interaction graph to capture the binding patterns between protein pockets and ligands. Guided by these patterns, we develop a cross-modal contrastive learning module to optimize the representation of pocket sequences. We also capture higher-order structural relations and atomic-level structural features through hypergraphs and atomic graphs of protein pockets, respectively. A cross-modal hierarchical fusion module fuses multi-modal embeddings from pocket sequences, pocket hypergraph structure, and pocket atomic graph structure. In addition, a pocket-conditioned molecule generator employs fused features as constraints for generating molecules possessing target characteristics. Experiments demonstrate that CoPocket outperforms existing methods across multiple key metrics, validating its effectiveness for molecular generation tasks.