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
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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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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Self-citationCharacterization of SARS-CoV-2 replication complex elongation and proofreading activity
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Predictive Analytics for Mortality: FSRNCA-FLANN Modeling Using Public Health Inventory Records
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Self-citationA new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study
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Repurposing Polyether Ionophores as a New-Class of Anti-SARS-Cov-2 Agents as Adjunct Therapy
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Medicinal Mushrooms and Their Bioactive Compounds: From Traditional Use to Therapeutic Potential
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Repurposing of Drugs Targeted Against COVID-19 Spike Receptor for Treatment: An In silico Approach
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Cordycepin and its Nucleoside Analogs for the Treatment of Systemic COVID-19 Infection
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Virus-induced senescence: A therapeutic target to mitigate severe progression of SARS-COV-2
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Systems medicine framework for repurposable drug combinations for COVID-19 comorbidities
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Therapeutic and Prophylactic Potential of Medicinal Mushrooms in COVID-19
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Unveiling the Multifaceted Antiviral Properties of Cordyceps
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Estimating the EGFR Ligand‐Binding Affinity and Pose via Molecular Docking Simulations
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Role of Machine Learning in Handling the COVID-19 Pandemic
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In Vitro and In Vivo Effectiveness of GPK/B2O, A Phytocomplex in Two Experimental Flu Virus Models
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Model exploration for discovering COVID-19 targeted traditional Chinese medicine
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Structure-Based Screening of Potential Drugs against SARS-CoV-2 Variants
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Differential Evolution for Docking Simulations
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Target-centric drug repurposing: a next-generation approach to confront antimicrobial resistance
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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
- ORCID
- 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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