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High Performance Computing Prediction of Potential Natural Product Inhibitors of SARS-CoV-2 Key Targets

18 June 2020, Version 1
Working Paper
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

This work describes using a supercomputer to perform virtual screening of natural products and natural products derivatives against several conformations of 3 proteins of SARS-CoC-2 : papain-like protease, main protease and spike protein. We analyze the common chemical features of the top molecules predicted to bind and describe the pharmacophores responsible for The University of Ala predicted binding.

Keywords

COVID19 Drugs
Natural products
drug discovery
Molecular modeling
pharmacophore
SARS-CoV-2
PLpro inhibitors
MPro
spike proteins

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Version History

Jun 18, 2020 Version 1

Version Notes

Version number 1

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The content is available under CC BY NC ND 4.0 [opens in a new tab]

DOI

10.26434/chemrxiv.12497693.v1
D O I: 10.26434/chemrxiv.12497693.v1 [opens in a new tab]

Funding

N/A

Author’s competing interest statement

N/A

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