Computational Estimation of Potential Inhibitors from the Known Drugs against the Main Protease of SARS-CoV-2

Nguyen Minh Tam, Pham Minh Quan, Nguyen Xuan Ha, Pham Cam Nam, Huong Thi Thu Phung

10.26434/chemrxiv.14102675.v1

Research curated

Abstract

The coronavirus disease (COVID-19) pandemic caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to a global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempted to computationally screen for possible medications for COVID-19 via rapidly estimate the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twentyseven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin, naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in COVID-19 therapy has thus been discussed.

Source: ChemRxiv

Related molecules

Name	Synonyms	Genes
Teniposide
Levoleucovorin
Naldemedine
Cabozantinib
Irinotecan
Etoposide	Toposar
3C-like protease	3CLpro, Mpro, SARS coronavirus main peptidase, 3CLpro-SARS-CoV-2	rep, ORF1a-1b
Daclatasvir

Related interactions

Target	Drug	Type	Result
3C-like protease	Teniposide	Computational	positive
3C-like protease	Levoleucovorin	Computational	positive
3C-like protease	Naldemedine	Computational	positive
3C-like protease	Cabozantinib	Computational	positive
3C-like protease	Irinotecan	Computational	positive
3C-like protease	Etoposide	Computational	positive
3C-like protease	Daclatasvir	Computational	positive