MotifGT-DTI: Pivotal Motif-Based Graph Transformer Model Improves Drug--Target Interaction Prediction

Predicting drug-target interactions (DTIs) plays an essential role in drug discovery and drug repurposing. Although significant performance improvements have been achieved in DTI prediction, existing methods have not fully explored the properties of protein and drug molecular structure to make results interpretable. In this study, we propose MotifGT-DTI, a novel motif-based model with a graph transformer (GT) for DTI prediction. Specifically, MotifGT-DTI captures complex molecular patterns of drug molecular graph motifs and protein 3-D pocket subgraphs with GT. To attain protein characteristics more comprehensively, MotifGT-DTI fuses 1-D sequence and 3-D structure features with cross-attention from two views. Then, the structural-level association patterns of drug molecules and proteins are connected via a bilinear attention network. Experimental results show that MotifGT-DTI achieves the best accuracy compared to state-of-the-art baselines on four public datasets. In the three cold-start scenarios, the prediction results provided by our method are competitive in accuracy, generalization ability, and stability, highlighting its promising potential for practical applications. Furthermore, the visualization study demonstrates that MotifGT-DTI finds functional molecular motifs and provides interpretability for predicted results. The datasets and codes are publicly available at https://github.com/Dimpleney/MotifGT-DTI.