<p></p><p>The ongoing pandemic of Coronavirus Disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a serious threat to global public health. Currently no approved drug or vaccine exists against SARS-CoV-2. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, is a time efficient approach to find effective drugs against SARS-CoV-2 in this emergency situation. Both experimental and computational approaches are being employed in drug repurposing with computational approaches becoming increasingly popular and efficient. In this study, we present a robust experimental design combining deep learning with molecular docking experiments to identify most promising candidates from the list of FDA approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2,440 FDA approved and 8,168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. We used a recently published open source AutoDock based high throughput screening platform virtualflow to reduce the time required to run around 50,000 docking simulations. A list of 49 most promising FDA approved drugs with best consensus KIBA scores and AutoDock vina binding affinity values against selected SARS-CoV-2 viral proteins is generated. Most importantly, anidulafungin, velpatasvir, glecaprevir, rifabutin, procaine penicillin G, tadalafil, riboflavin 5’-monophosphate, flavin adenine dinucleotide, terlipressin, desmopressin, elbasvir, oxatomide, enasidenib, edoxaban and selinexor demonstrate highest predicted inhibitory potential against key SARS-CoV-2 viral proteins.</p><p></p>

<p></p><p>The ongoing pandemic of Coronavirus Disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a serious threat to global public health. Currently no approved drug or vaccine exists against SARS-CoV-2. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, is a time efficient approach to find effective drugs against SARS-CoV-2 in this emergency situation. Both experimental and computational approaches are being employed in drug repurposing with computational approaches becoming increasingly popular and efficient. In this study, we present a robust experimental design combining deep learning with molecular docking experiments to identify most promising candidates from the list of FDA approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2,440 FDA approved and 8,168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. We used a recently published open source AutoDock based high throughput screening platform virtualflow to reduce the time required to run around 50,000 docking simulations. A list of 49 most promising FDA approved drugs with best consensus KIBA scores and AutoDock vina binding affinity values against selected SARS-CoV-2 viral proteins is generated. Most importantly, anidulafungin, velpatasvir, glecaprevir, rifabutin, procaine penicillin G, tadalafil, riboflavin 5’-monophosphate, flavin adenine dinucleotide, terlipressin, desmopressin, elbasvir, oxatomide, enasidenib, edoxaban and selinexor demonstrate highest predicted inhibitory potential against key SARS-CoV-2 viral proteins.</p><p></p>