A multilevel approach for screening natural compounds as an antiviral agent for COVID-19.
Comput Biol Chem
; 98: 107694, 2022 Jun.
Article
in English
| MEDLINE | ID: covidwho-1944667
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Antiviral Agents
/
COVID-19 Drug Treatment
Type of study:
Prognostic study
Topics:
Traditional medicine
Limits:
Humans
Language:
English
Journal:
Comput Biol Chem
Journal subject:
Biology
/
Medical Informatics
/
Chemistry
Year:
2022
Document Type:
Article
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