Hierarchical Virtual Screening of SARS-CoV-2 Main Protease Potential Inhibitors: Similarity Search, Pharmacophore Modeling, and Molecular
Docking Study
{"title":"Hierarchical Virtual Screening of SARS-CoV-2 Main Protease Potential Inhibitors: Similarity Search, Pharmacophore Modeling, and Molecular\nDocking Study","authors":"H. Mando, Iyad allous","doi":"10.2174/0122113525280410240106122715","DOIUrl":null,"url":null,"abstract":"\n\nThe outbreak of COVID-19 caused by severe acute respiratory syndrome\ncoronavirus2 (SARS-CoV-2) resulted in a widespread pandemic. Various approaches involved\nthe repositioning of antiviral remedies and other medications. Several therapies, including\noral antiviral treatments, represent some approaches to adapting to the long existence of the\nCOVID-19 pandemic. In silico studies provide valuable insights throughout drug discovery and\ndevelopment in compliance with global efforts to overcome the pandemic. The main protease is\nan essential target in the viral cycle. Computer-aided drug design accelerates the identification\nof potential treatments, including oral therapy.\n\n\n\nThis work aims to identify potential SARS-CoV-2 main protease inhibitors using different\naspects of in silico approaches.\n\n\n\nIn this work, we conducted a hierarchical virtual screening of SARS-CoV-2 main protease\ninhibitors. A similarity search was conducted to screen molecules similar to the inhibitor\nPF-07321332. Concurrently, structure-based pharmacophores, besides ligand-based pharmacophores,\nwere derived. A drug-likeness filter filtered the compounds retrieved from similarity\nsearch and pharmacophore modeling before being subjected to molecular docking. The candidate\nmolecules that showed higher affinity to the main protease than the reference inhibitor were\nfurther filtered by absorption, distribution, metabolism, and excretion (ADME) parameters.\n\n\n\nAccording to binding affinity and ADME analysis, four molecules (CHEMBL218022,\nPubChem163362029, PubChem166149100, and PubChem 162396459) were prioritized as\npromising hits. The compounds above were not reported before; no previous experimental studies\nand bioactive assays are available.\n\n\n\nOur time-saving approach represents a strategy for discovering novel SARS-CoV-\n2 main protease inhibitors. The ultimate hits may be nominated as leads in discovering novel\nSARS-CoV-2 main protease inhibitors.\n","PeriodicalId":7951,"journal":{"name":"Anti-Infective Agents","volume":"671 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anti-Infective Agents","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0122113525280410240106122715","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
The outbreak of COVID-19 caused by severe acute respiratory syndrome
coronavirus2 (SARS-CoV-2) resulted in a widespread pandemic. Various approaches involved
the repositioning of antiviral remedies and other medications. Several therapies, including
oral antiviral treatments, represent some approaches to adapting to the long existence of the
COVID-19 pandemic. In silico studies provide valuable insights throughout drug discovery and
development in compliance with global efforts to overcome the pandemic. The main protease is
an essential target in the viral cycle. Computer-aided drug design accelerates the identification
of potential treatments, including oral therapy.
This work aims to identify potential SARS-CoV-2 main protease inhibitors using different
aspects of in silico approaches.
In this work, we conducted a hierarchical virtual screening of SARS-CoV-2 main protease
inhibitors. A similarity search was conducted to screen molecules similar to the inhibitor
PF-07321332. Concurrently, structure-based pharmacophores, besides ligand-based pharmacophores,
were derived. A drug-likeness filter filtered the compounds retrieved from similarity
search and pharmacophore modeling before being subjected to molecular docking. The candidate
molecules that showed higher affinity to the main protease than the reference inhibitor were
further filtered by absorption, distribution, metabolism, and excretion (ADME) parameters.
According to binding affinity and ADME analysis, four molecules (CHEMBL218022,
PubChem163362029, PubChem166149100, and PubChem 162396459) were prioritized as
promising hits. The compounds above were not reported before; no previous experimental studies
and bioactive assays are available.
Our time-saving approach represents a strategy for discovering novel SARS-CoV-
2 main protease inhibitors. The ultimate hits may be nominated as leads in discovering novel
SARS-CoV-2 main protease inhibitors.
期刊介绍:
Anti-Infective Agents publishes original research articles, full-length/mini reviews, drug clinical trial studies and guest edited issues on all the latest and outstanding developments on the medicinal chemistry, biology, pharmacology and use of anti-infective and anti-parasitic agents. The scope of the journal covers all pre-clinical and clinical research on antimicrobials, antibacterials, antiviral, antifungal, and antiparasitic agents. Anti-Infective Agents is an essential journal for all infectious disease researchers in industry, academia and the health services.