A drug–target affinity prediction model integrating multimodal feature fusion and structural modeling

. In particular, the model exhibits strong generalization and ranking performance on structurally complex datasets such as PDBbind and BindingDB. Our approach offers a more accurate and interpretable solution for modeling drug-target interactions, with promising potential for real-world drug discovery applications. The complete implementation of our framework is publicly available at https://github.com/xu1nan/MUSDTA.