Structure Enhanced Protein-Drug Interaction Prediction using Transformer and Graph Embedding

The identification of protein-drug interaction plays an important role in the early stage of drug discovery. Recently, deep learning has been introduced into this area and gained excellent results, which mainly benefits from effective representations of protein and drug. However, the sparseness of raw input data builds the fundamental barrier for protein and drug representation learning. In this paper, we propose an end-to-end model representing better the characteristics of protein and drug to estimate binding affinity between protein and drug. By incorporating protein sequence, protein contact map and drug SMILES, our model achieves better results on PDBbind dataset as compared to 3D structure based classical deep model Pafnucy. Moreover, we explore the effects of different parts of our model with ablation study and find that by combining all the modules of our model we can get the best predictive results.