Cross-Modal Semantic Alignment for Cold-Start Drug-Target Interaction Prediction

Drug-target interaction (DTI) prediction is essential for drug discovery, yet existing models often suffer from limited generalization when drugs or targets are unseen during training. This challenge is particularly severe in cold-start settings, where interaction records are unavailable for novel entities and effective semantic transfer between chemical and biological spaces is required. To address this problem, we propose CMA-DTI, a cross-modal semantic alignment framework for cold-start drug-target interaction prediction. CMA-DTI first extracts drug and protein representations from pretrained sequence models, and then it uses a dual-channel encoder to capture local functional patterns and global contextual dependencies. A bidirectional cross-modal alignment module is further introduced to model interactions between drug functional groups and protein binding regions, enabling fine-grained semantic transfer across heterogeneous modalities. Experiments on four benchmark data sets show that CMA-DTI achieves competitive performance in warm-start settings and improves generalization in challenging target cold-start and blind-start scenarios. Ablation studies and structural visualizations further support the contribution of the proposed modules and suggest residue-level interpretability for protein-ligand binding analysis.