Cross Attention and Intra-layer Attention in Heterogeneous Graph Neural Networks for Drug-Target Interaction Prediction

In recent years, computational prediction of drug-target interactions (DTIs) has become essential for drug discovery and repositioning. However, traditional experimental approaches for DTI identification are time-consuming and costly. To address this, many machine learning-based methods have been developed, yet most existing models neglect important information interaction between drugs and targets in drug-target pairs(DTPs) during drug-target interaction. In this study, we propose a novel cross-attention and intra-layer attention mechanism within a heterogeneous graph neural network (CAIHGNN) for DTI prediction. The cross-attention mechanism allows for dynamic learning of feature correlations between drugs and targets, while the intra-layer attention captures both explicit and implicit interactions within DTPs. Additionally, we introduce a drug-target pair correlation graph to exploit high-order interactions between DTPs. Extensive experiments on two biological heterogeneous datasets demonstrate the superior performance of our proposed method in accurately predicting DTIs. Furthermore, the model exhibits robust generalization in case study, showing promise for real-world drug discovery applications.