Cited by 54Computational Drug Discovery MethodsSARS-CoV-2 and COVID-19 Researchvaccines and immunoinformatics approaches

Repurposing potential of FDA‐approved and investigational drugs for COVID‐19 targeting SARS‐CoV‐2 spike and main protease and validation by machine learning algorithm

Verma, Akalesh Kumar, Aggarwal, Rohit·2021·Chemical Biology & Drug Design

Abstract

The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA‐approved and investigational) against SARS‐CoV‐2 target proteins that facilitates viral entry and replication into the host body. To evaluate molecular affinities between antiviral drug candidates and SARS‐CoV‐2 associated target proteins such as spike protein (S) and main protease (M pro), a molecular interaction simulation was performed by docking software (MVD) and subsequently the applicability score was calculated by machine learning algorithm. Furthermore, the STITCH algorithm was used to predict the pharmacology network involving multiple pathways of active drug candidate(s). Pharmacophore features of active drug(s) molecule was also determined to predict structure–activity relationship (SAR). The molecular interaction analysis showed that cordycepin has strong binding affinities with S protein (−180) and M pro proteins (−205) which were relatively highest among other drug candidates used. Interestingly, compounds with low IC 50 showed high binding energy. Furthermore, machine learning algorithm also revealed high applicability scores (0.42–0.47) of cordycepin. It is worth mentioning that the pharmacology network depicted the involvement of cordycepin in different pathways associated with bacterial and viral diseases including tuberculosis, hepatitis B, influenza A, viral myocarditis, and herpes simplex infection. The embedded pharmacophore features with cordycepin also suggested strong SAR. Cordycepin's anti‐SARS‐CoV‐2 activity indicated 65% (E‐gene) and 42% (N‐gene) viral replication inhibition after 48h of treatment. Since, cordycepin has both preclinical and clinical evidences on antiviral activity, in addition the present findings further validate and suggest repurposing potential of cordycepin against COVID‐19.

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Crossref · 10.1111/cbdd.13812

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  • A machine learning-based assessment of 45 FDA-approved drugs failed to predict significant direct anti-viral activity of navitoclax and dasatinib.

    “A machine learning-based assessment of 45 FDA-approved drugs for their potential to be repurposed as antiviral agents in COVID-19 failed to predict a significant direct anti-viral activity of navitoclax and dasatinib, further underscoring that the mode of action of these agents in COVID-19 might be indirect, for example, through the selective elimination of senescent cells117.”

    From COVID-19 and cellular senescence (2023) · cites this paper as [117]

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Authors
Verma, A. K., & Aggarwal, R.
Affiliations
  • Cotton University (IN)
  • Cosmic Cordycep Farms Badarpur Said Tehsil Tigaon Faridabad Haryana India
Corresponding author
Verma, Akalesh Kumar
Venue
Chemical Biology & Drug Design
Publisher
Wiley
Published
2020-12-22
Volume
97
Issue
4
Pages
836-853
ISSN
  • 1747-0277 (ISSN-L)
  • 1747-0285
OpenAlex
W3112868751
Cited by
54
References
72
Citations by year
2026: 4 · 2025: 3 · 2024: 8 · 2023: 9 · 2022: 15 · 2021: 15
FWCI
4.83
Type
journal article
Language
en
Open access
closed
Locations
  • Chemical Biology & Drug Design (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
drug repurposing, COVID-19 pandemic, SARS-CoV-2, spike protein, main protease (Mpro), cordycepin, antiviral drug candidates, molecular docking, pharmacophore features, structure-activity relationship, viral replication inhibition, FDA-approved drugs, investigational drugs
MeSH terms
Machine Learning, COVID-19, SARS-CoV-2, Coronavirus M Proteins, COVID-19 Drug Treatment, Antiviral Agents, Binding Sites, Cell Line, Cell Survival, Deoxyadenosines, Humans, Protease Inhibitors, Structure-Activity Relationship, Viral Matrix Proteins, Drug Repositioning, Molecular Docking Simulation, Spike Glycoprotein, Coronavirus

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