A Multi-Feature and Dual-Attribute Interaction Aggregation Model for Predicting Drug-Target Interactions

Discovering potential drug-target interactions is crucial for advancing pharmacology. In recent years, the development of large-scale DTI datasets has propelled advancements in DTI prediction computational methods. Various deep learning approaches for interaction prediction often rely on sequence data or structural complexity, yet the synergistic integration of diverse bioinformatics and binding site data remains underexploited, constraining prediction precision. Therefore, a novel approach to integrate available data is required to enhance DTI prediction performance. In this paper, we present a novel aggregation prediction model named MDiDTI, designed to facilitate multi-attribute dual interaction learning. The multi-head self-attention interaction network extracts substructure information of drug molecules and pocket information of targets from biomedical data, enabling spatial-level learning of structural attributes. Meanwhile, the dual-weight mapping network aggregates the chemical semantic features of drug-target pairs, facilitating semantic attribute learning at the sequence level. Lastly, the model combines structural and semantic attributes to compute the interaction values for DTI tasks. Performance evaluation metrics were conducted on three mainstream datasets: BioSNAP, BindingDB, and Human. Experimental results indicate that MDiDTI outperforms existing methods and serves as a reliable and highly generalizable tool for DTI prediction.