These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
3 files

Supercomputer-aided Drug Repositioning at Scale: Virtual Screening for SARS-CoV-2 Protease Inhibitor

submitted on 09.04.2020, 01:42 and posted on 10.04.2020, 06:21 by Sangjae Seo, Jung Woo Park, Dosik An, Junwon Yoon, Hyojung Paik, Soonwook Hwang
Coronavirus diseases (COVID-19) outbreak has been labelled a pandemic. For the prioritization of treatments to cope with COVID-19, it is important to conduct rapid high-throughput screening of chemical compounds to repurposing the approved drugs, such as repositioning of chloroquine (Malaria drug) for COVID-19. In this study, exploiting supercomputer resource, we conducted high-throughput virtual screening for potential repositioning candidates of the protease inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using the three dimensional structure of main protease (Mpro) of SARS-CoV-2, we evaluated binding affinity between Mpro and drug candidates listed in SWEETLEAD library and ChEMBL database. Docking scores of 19,168 drug molecules at the active site of Mpro were calculated using Autodock Vina package. Among the calculated result, we selected 43 drug candidates and ran molecular dynamics (MD) simulation to further investigate protein-drug interaction. Among compounds that bind to the active site of SARS-CoV-2, we finally selected the 8 drugs showing the highest binding affinity; asunaprevir, atazanavir, dasabuvir, doravirine, fosamprenavir, ritonavir, voxilaprevir and amprenavir, which are the antiviral drugs of hepatitis C virus or human immunodeficiency virus. We expect that the present study provides comprehensive insights into the development of antiviral medication, especially for the treatment of COVID-19.

* Attached excel file contains a full list of results of docking calculations


This work was supported by the National Supercomputing Center with supercomputing resources including technical support TS-2020-RE-0012.


Email Address of Submitting Author


Korean Institute of Science and Technology Information


Republic of Korea

ORCID For Submitting Author


Declaration of Conflict of Interest

no conflict of interest