Transformer and Graph Transformer-Based Prediction of Drug-Target Interactions

Background: As we all know, finding new pharmaceuticals requires a lot of time and money, which has compelled people to think about adopting more effective approaches to locate drugs. Researchers have made significant progress recently when it comes to using Deep Learning (DL) to create DTI. Methods: Therefore, we propose a deep learning model that applies Transformer to DTI prediction. The model uses a Transformer and Graph Transformer to extract the feature information of protein and compound molecules, respectively, and combines their respective representations to predict interactions. Results: We used Human and C.elegans, the two benchmark datasets, evaluated the proposed method in different experimental settings and compared it with the latest DL model. Conclusion: The results show that the proposed model based on DL is an effective method for the classification and recognition of DTI prediction, and its performance on the two data sets is significantly better than other DL based methods.