Hierarchical Cross-Modal Fusion for Drug-Target Binding Affinity Prediction

Predicting drug-target affinity is a fundamental task in computational drug discovery. Existing methods often rely on a single view or perform shallow multimodal fusion, which may lead to view-specific bias and insufficient coordination among heterogeneous representations. As a result, they often fail to fully capture the complementary biochemical and structural cues underlying drug-target interactions. In this work, we propose TriM-DTA, a hierarchical cross-modal fusion framework for predicting drug-target binding affinity. TriM-DTA jointly models molecular sequence, 2D topology, and 3D geometry of both drugs and targets, and progressively integrates these heterogeneous views into a unified affinity-aware representation. By coordinating structural information before deeper cross-modal interaction, TriM-DTA reduces cross-view mismatch and enables more effective coupling of semantic, topological, and geometric cues. Experimental results demonstrate that TriM-DTA consistently outperforms existing methods and exhibits strong generalizability to unseen drugs and proteins. In addition, visualization analyses reveal that TriM-DTA captures binding-related patterns and highlights chemically meaningful substructures, providing interpretable evidence for its predictions. These findings suggest that coordinated multimodal representation learning provides a useful basis for broader molecular modeling tasks in drug discovery.