Computational Approaches to Discover Novel Natural Compounds for SARS-CoV-2 Therapeutics

Scientists all over the world are facing a challenging task of finding effective therapeutics for the coronavirus disease (COVID-19). One of the fastest ways of finding putative drug candidates is the use of computational drug discovery approaches. The purpose of the current study is to retrieve natural compounds that have obeyed to drug-like properties as potential inhibitors. Computational molecular modelling techniques were employed to discover compounds with potential SARS-CoV-2 inhibition properties. Accordingly, the InterBioScreen (IBS) database was obtained and was prepared by minimizing the compounds. To the resultant compounds, the absorption, distribution, metabolism, excretion and toxicity (ADMET) and Lipinski's Rule of Five was applied to yield drug-like compounds. The obtained compounds were subjected to molecular dynamics simulation studies to evaluate their stabilities. In the current article, we have employed the docking based virtual screening method using InterBioScreen (IBS) natural compound database yielding two compounds has potential hits. These compounds have demonstrated higher binding affinity scores than the reference compound together with good pharmacokinetic properties. Additionally, the identified hits have displayed stable interaction results inferred by molecular dynamics simulation results. Taken together, we advocate the use of two natural compounds, STOCK1N-71493 and STOCK1N-45683 as SARS-CoV-2 treatment regime.

Automated summary

Scientists utilized computational drug discovery approaches to identify natural compounds with potential as inhibitors for COVID-19. After a series of screening, simulation, and evaluation steps, two compounds, STOCK1N-71493 and STOCK1N-45683, were recognized as having potential hits with higher binding affinity and good pharmacokinetic properties for SARS-CoV-2 treatment.