Interpretable and Generalizable Attention-Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites: SARS-CoV-2 Case Study and in-Lab Validation

In this study, we introduce and implement an interpretable graph-based deep learning prediction model, which utilizes protein binding sites along with self-attention to learn which protein binding sites interact with a given ligand. Our proposed model enables interpretability by identifying the protein binding sites that contribute the most towards the Drug-Target Interaction. Results on three benchmark datasets show improved performance compared to previous graph-based models. More significantly, unlike previous studies our model performance remains close to the optimal performance when tested with new proteins (ie., high generalizablity). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict binding interaction of some candidate compounds with a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally-predicted and experimentally-observed (measured) DTIs illustrates the potential of our method as an effective pre-screening tool in drug re-purposing applications.