PropertyValue
?:abstract
  • Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CL(Pro) in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.
is ?:annotates of
?:creator
?:doi
  • 10.1093/bib/bbaa260
?:doi
?:journal
  • Brief_Bioinform
?:license
  • no-cc
?:pdf_json_files
  • document_parses/pdf_json/f92d3793a384ebd9eaaf77487abbc73e64222cf4.json
?:pmc_json_files
  • document_parses/pmc_json/PMC7665328.xml.json
?:pmcid
?:pmid
?:pmid
  • 33078827.0
?:publication_isRelatedTo_Disease
is ?:relation_isRelatedTo_publication of
?:sha_id
?:source
  • Medline; PMC
?:title
  • MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs
?:type
?:year
  • 2020-10-20

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