IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.

Conventional drug discovery is expensive and takes a long period. Drug-target interaction (DTI) prediction through computational methods significantly improves efficiency and reduces costs, holding substantial research value. Despite progress in existing prediction methods, two major challenges remain: first, most methods fail to effectively combine shallow and deep features of protein sequences, overlooking the synergistic effect of both; second, existing feature fusion techniques are relatively simple and struggle to fully capture the complexity and richness of fused features. We suggest an interpretable hybrid deep feature fusion network (IHDFN) as a solution to these problems. In the hybrid deep feature extraction module for protein sequences, shallow and deep features of protein sequences are extracted through two distinct views respectively, which capture multi-level information of proteins comprehensively. To further enhance the feature fusion effect, we introduce the StarNet fusion model in this module, enabling efficient fusion of shallow and deep features and enriching feature representation. To further improve the representation power of drug characteristics and the stability of the model, we use a graph convolutional network (GCN) in the drug feature extraction section in conjunction with residual connections and layer normalization. Furthermore, by integrating multimodal features from drugs and proteins utilizing an attention mechanism in the heterogeneous feature fusion module, we increase the complexity of features and achieve interpretability in predictions by attention focusing. Finally, we experimented on three datasets, and the findings indicate that IHDFN has exceptional performance and robustness compared to other cutting-edge techniques, underscoring its great promise and usefulness in DTI tasks. The code for this study is available on GitHub at https://github.com/wangqhfff/IHDFN.git .