Drug–Target Affinity Prediction Based on Cross-Modal Fusion of Text and Graph

Drug–target affinity (DTA) prediction is a critical step in virtual screening and significantly accelerates drug development. However, existing deep learning-based methods relying on single-modal representations (e.g., text or graphs) struggle to fully capture the complex interactions between drugs and targets. This study proposes CM-DTA, a cross-modal feature fusion model that integrates drug textual representations and molecular graphs with target protein amino acid sequences and structural graphs, enhancing feature diversity and expressiveness. The model employs the multi-perceptive neighborhood self-attention aggregation strategy to capture first- and second-order neighborhood information, overcoming limitations in graph isomorphism networks (GIN) for structural representation. The experimental results on the Davis and KIBA datasets show that CM-DTA significantly improves the performance of drug–target affinity prediction, achieving higher accuracy and better prediction metrics compared to state-of-the-art (SOTA) models.