Preprint Communication Version 1 This version is not peer-reviewed

Virtual High Throughput Screening Based Prediction of Potential Drugs for COVID-19

Version 1 : Received: 25 February 2020 / Approved: 28 February 2020 / Online: 28 February 2020 (02:38:05 CET)
Version 2 : Received: 8 March 2020 / Approved: 9 March 2020 / Online: 9 March 2020 (02:29:04 CET)

How to cite: Talluri, S. Virtual High Throughput Screening Based Prediction of Potential Drugs for COVID-19. Preprints 2020, 2020020418 (doi: 10.20944/preprints202002.0418.v1). Talluri, S. Virtual High Throughput Screening Based Prediction of Potential Drugs for COVID-19. Preprints 2020, 2020020418 (doi: 10.20944/preprints202002.0418.v1).

Abstract

SARS-CoV-2 is a betacoronavirus that was first identified during the Wuhan COVID-19 epidemic in 2019. It was listed as a potential global health threat by WHO due to high mortality, high basic reproduction number and lack of clinically approved drugs and vaccines for COVID-19. The genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three dimensional structure of the Main protease (Mpro) are available. The reported structure of the target Mpro was utilized in this study to identify potential drugs for COVID-19 using virtual high throughput screening. The results of this study confirm earlier preliminary reports based on studies of homologs that some of the drugs approved for treatment of other viral infections also have the potential for treatment of COVID-19. Approved anti-viral drugs that target proteases were ranked for potential effectiveness against COVID-19 and novel candidates for drug repurposing were identified.

Subject Areas

virtual HTS; docking; drug reposition; drug repurposing; coronavirus; COVID-19; 2019-nCoV; SARS-CoV-2

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