Abstract
The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those infected. Fortunately, many efforts have been made and several effective drugs have been identified. The rapidly increasing amount of data is of great help for training an effective and specific deep learning model. In this study, we propose a multi-task deep learning model for the purpose of screening commercially available and effective inhibitors against SARS-CoV-2. First, we pretrained a model on several heterogenous protein-ligand interaction datasets. The model achieved competitive results on some benchmark datasets. Next, a coronavirus-specific dataset was collected and used to fine-tune the model. Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets. Overall, twenty compounds were listed as potential inhibitors. We further explored the model interpretability and exhibited the predicted important binding sites. Based on this prediction, molecular docking was also performed to visualize the binding modes of the selected inhibitors.
Keywords: SARS-CoV-2; deep learning; drug discovery; multi-task learning; protein–ligand interaction.
【저자키워드】 SARS-CoV-2, Drug discovery, deep learning, multi-task learning, protein–ligand interaction., 【초록키워드】 COVID-19, Drug discovery, deep learning, Infection, molecular docking, drug, inhibitors, effective drugs, outbreak, death, dataset, targets, binding, Interaction, binding modes, Compound, severe symptoms, help, effort, effective inhibitor, SARS-CoV-2 protein, datasets, heterogenous, protein-ligand interaction, Prevent, effective, fine-tune, selected, predicted, identify, performed, was used, caused, was collected, exhibited, can be used, rising, binding mode, effective drug, 【제목키워드】 prediction, learning, Model, deep, Potential,