Investigating drug-target interactions plays a critical role in drug design and discovery. The vast chemical and proteomic space, along with the cost associated with invirto experiments motivate the use of computational methods to narrow down the search space for novel interaction of drug target pairs. Among all computational methods, deep learning algorithms have gained increased attention due to their power in automatically learning and extracting feature representations, and therefore identifying, processing and extrapolating complex hidden interactions between drugs and targets. In this study, we introduce and implement a new graph-based prediction model called AttentionSiteDTI. Our proposed model utilize the binding sites (pockets) of the proteins as the input for the target protein, and it uses a self-attention mechanism to make the model learn which binding sites of the protein interact with a given ligand. This, indeed, complements the black-box nature of deep learning-based methods and enables interpretability, while achieving state of the art results in drug target interaction prediction task on three datasets. The AttentionSite DTI achieves AUC of 0.97 (for seen proteins), 0.94 (for unseen proteins) in the customized BindingDB dataset, 0.971 in the DUD-E dataset, and 0.991 in the human dataset. In general, the prediction results on these datasets show the superiority of our AttentionSiteDTI compared to previous graph-based models, and our ablation studies proves the effectiveness of our proposed model in prediction of drug-target interactions. In addition, through multidisciplinary collaboration in this work, 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, and then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally-predicted and experimentally observed (measured) drug-target interactions illustrates the potential of our AttentionSiteDTI as effective pre-screening tool in drug repurposing applications.
Generating novel molecule for target protein (SARS-CoV-2) using drug–target interaction based on graph neural network
