CMA-DTI: a cross-modal fusion and attentive interaction network for interpretable drug-target interaction prediction.

Background: Drug-target interaction (DTI) prediction is an important task in early-stage drug discovery. Although deep learning methods have improved predictive performance, effectively integrating heterogeneous drug representations and providing interpretable evidence for local interaction patterns remain challenging. Methods: We propose CMA-DTI, a cross-modal fusion and attentive interaction framework for DTI prediction. CMA-DTI integrates GCN-based molecular graph representations, ChemBERTa-derived SMILES representations, and ESM-2 protein residue embeddings. An intra-drug cross-attention module models soft relevance patterns between graph nodes and SMILES tokens, while a drug-protein multi-head attention module captures local relevance between fused drug nodes and protein residues. Results: On BindingDB and BioSNAP, CMA-DTI achieved competitive performance compared with representative machine learning and deep learning baselines. Cold-drug and cold-target evaluations showed that the model retained predictive ability under unseen-drug and unseen-target settings. Ablation results indicated that intra-drug cross-attention performed better than concatenation, addition, and gated fusion. In a representative structural case, attention-ranked residues showed moderate enrichment in ligand-binding pocket residues compared with random rankings. Conclusion: CMA-DTI provides a practical framework for multimodal DTI prediction by combining graph structure, chemical language representations, and protein language model embeddings. Its attention patterns offer hypothesis-generating molecular relevance evidence, but should not be interpreted as causal mechanistic explanations.