Node features (atom).

Drug-target interaction (DTI) prediction is essential for the development of novel drugs and the repurposing of existing ones. However, when the features of drug and target are applied to biological networks, there is a lack of capturing the relational features of drug-target interactions. And the corresponding multimodal models mainly depend on shallow fusion strategies, which results in suboptimal performance when trying to capture complex interaction relationships. Therefore, this study proposes a novel framework named KG-MACNF. This framework utilizes knowledge graph embedding (KGE) techniques to capture multi-level relational features of entities in large-scale biological networks. Simultaneously, our innovative PoolGAT network, along with CTD descriptors, is employed to extract drug structural features and protein sequence information. Finally, by employing our innovative nonlinear-driven cross-modal attention fusion network, the framework efficiently integrates these multimodal data and generates the final DTI prediction results. Experiments on two publicly available datasets, Yamanishi_08’s and BioKG, demonstrate the substantial advantages of KG-MACNF in DTI prediction. KG-MACNF demonstrates robust stability, especially under imbalanced data conditions. This study successfully overcomes the bottlenecks of prior models in utilizing modality information and feature complementarity, providing a more accurate tool for drug discovery and DTI prediction.