A multilevel approach for screening natural compounds as an antiviral agent for COVID-19.

Comput Biol Chem

Department of Inorganic Chemistry, Sofia University "St. Kl. Ohridski", Sofia, Bulgaria; Chemistry Department, University of Fribourg, Fribourg, Switzerland. Electronic address:

Published: June 2022

The COVID-19 has a worldwide spread, which has prompted concerted efforts to find successful drug treatments. Drug design focused on finding antiviral therapeutic agents from plant-derived compounds which may disrupt the attachment of SARS-CoV-2 to host cells is with a pivotal need and role in the last year. Herein, we provide an approach based on drug design methods combined with machine learning approaches to classify and discover inhibitors for COVID-19 from natural products. The spike receptor-binding domain (RBD) was docked with database of 125 ligands. The docking protocol based on several steps was performed within Autodock Vina to identify the high-affinity binding mode and to reveal more insights into interaction between the phytochemicals and the RBD domain. A protein-ligand interaction analyzer has been developed. The drug-likeness properties of explored inhibitors are analyzed in the frame of exploratory data analyses. The developed computational protocol yielded a comprehensive pipeline for predicting the inhibitors to prevent the entry RBD region.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090871PMC
http://dx.doi.org/10.1016/j.compbiolchem.2022.107694DOI Listing

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