Multiscale cross modal fusion and cascaded signal propagation for drug target interaction prediction.

Accurate prediction of drug-target interaction (DTI) facilitates the translation of lead compounds into clinical therapeutics. Despite progress in deep learning, two challenges remain: the representation gap between heterogeneous drug and protein modalities and insufficient use of complementary knowledge from pretrained encoders. To address these issues, we propose SF-DTI, a unified framework that integrates multi-encoder protein fusion, structure-semantic drug alignment, and progressive cross-modal refinement for DTI prediction. Specifically, SF-DTI captures global protein semantics from multiple pretrained protein language models, aligns graph-based structural features with pretrained semantic embeddings for drugs under subspace constraints, and progressively refines drug-protein interaction representations through cascaded cross-modal signal propagation. Experiments on six benchmark datasets under standard, cold-start, and cross-domain settings demonstrate that SF-DTI achieves competitive performance relative to seven state-of-the-art baselines across evaluation scenarios. These results suggest that SF-DTI is a promising framework for DTI prediction and may support virtual screening, drug repositioning, and drug discovery.