MSCMLCIDTI: Drug–Target Interaction Prediction Based on Multiscale Feature Extraction and Deep Interactive Attention Fusion Mechanisms

Abstract

Drug–target interaction prediction serves as a crucial component in accelerating drug discovery. To overcome current limitations in deep learning approaches, specifically the inadequate representation of local features and insufficient modeling of drug and target information interactions, we propose a multiscale feature extraction coupled multilayer cross-interaction network (MSCMLCIDTI). The model uses multiscale convolutional blocks to extract structural fingerprints of drug compounds and amino acid sequences at different scales for multigranularity pattern recognition across spatial domains, followed by gated attention to obtain multidimensional features. This multidimensional feature extraction enhances the model's capability to identify critical binding sites between pharmacological compounds and their biological targets. Furthermore, we implement a deep cross-interaction mechanism utilizing multilayer attention-based interactions to model complex relationships between distinct drug substructures and protein fragments. This design empowers accurate identification of sophisticated interaction signatures in pharmaceutical target complexes. Comprehensive validation across four open-access benchmark datasets reveals our framework's superior predictive accuracy compared to existing leading-edge models.