A critical overview of computational approaches employed for COVID-19 drug discovery
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We cover diverse methodologies, computational approaches, and case studies illustrating the ongoing efforts to develop viable drug candidates for treatment of COVID-19.
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QSAR methodologies can be automatically deployed against thousands of target bioactivities.
“QSAR methodologies can be automatically deployed against thousands of target bioactivities (82).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [82]
The ‘Deep learning approaches for SBDD’ section describes a platform that generated QSAR models and predicted docking scores for molecules against 12 target proteins. It does not establish that QSAR methods can be automatically deployed against thousands of target bioactivities; the large numbers reported refer to molecules, not bioactivities. The indexed passages provide no page number. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAmong research for COVID-19 drug discovery, many cases have been either exclusively computational or computer-aided experimental studies.
“Among research for COVID-19 drug discovery, many cases have been either exclusively computational or computer-aided experimental studies [78].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [78]
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Gentile, F., Yaacoub, J. C., Gleave, J., Fernandez, M., Ton, A.-T., Ban, F., Stern, A., & Cherkasov, A. (2022) · Nature Protocols · doi:10.1038/s41596-021-00659-2 · Cited by 397 · see citing works
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“QSAR technologies are an integral part of the technologies deployed to hunt for anti-COVID-19 therapies (81).”
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- Authors
- Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., Oprea, T. I., Poroikov, V., Schneider, G., Todd, M. H., Varnek, A., Winkler, D. A., Zakharov, A. V., Cherkasov, A., & Tropsha, A.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of California San Diego (US)
- Universidade Federal de Goiás (BR)
- BenevolentAI (United Kingdom) (GB)
- Collaborations Pharmaceuticals (United States) (US)
- North Carolina State University (US)
- Carnegie Mellon University (US)
- Stony Brook University (US)
- Department of Pharmacy, National Autonomous University of Mexico, Mexico City, DF, Mexico
- Michigan State University (US)
- University of Copenhagen (DK)
- Institute of Biomedical Chemistry (RU)
- ETH Zurich (CH)
- UCL School of Pharmacy (GB)
- Hokkaido University (JP)
- University of Nottingham (GB)
- National Center for Advancing Translational Sciences (US)
- University of British Columbia (CA)
- Corresponding author
- Cherkasov, Artem
- Tropsha, Alexander
- ORCID
- Muratov, Eugene N.
- Amaro, Rommie
- Andrade, Carolina H.
- Brown, Nathan
- Ekins, Sean
- Fourches, Denis
- Isayev, Olexandr
- Kozakov, Dima
- Medina-Franco, José L.
- Merz, Kenneth M.
- Oprea, Tudor I.
- Poroikov, Vladimir
- Schneider, Gisbert
- Todd, Matthew H.
- Varnek, Alexandre
- Winkler, David A.
- Zakharov, Alexey V.
- Cherkasov, Artem
- Tropsha, Alexander
- Venue
- Chemical Society Reviews
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2021
- Volume
- 50
- Issue
- 16
- Pages
- 9121-9151
- ISSN
- 0306-0012 (ISSN-L)
- 1460-4744
- PMC
- PMC8371861
- OpenAlex
- W3176005660
- Cited by
- 206
- References
- 193
- Citations by year
- 2026: 12 · 2025: 26 · 2024: 32 · 2023: 54 · 2022: 55 · 2021: 27
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Chemical Society Reviews (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (acceptedVersion, public-domain)
- UCL Discovery (University College London) (acceptedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (acceptedVersion, cc-by-nc)
- Figshare (submittedVersion, cc-by-nc)
- HAL (submittedVersion, other-oa)
- La Trobe University
- ETH Zürich Research Collection
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (205321_182176)
- National Institutes of Health (U01CA207160, U01CA239108, U24 CA224370, U24 TR002278, R44GM122196-02A1, GM132826)
- Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México (V200121)
- National Science Foundation (MCB-2032054, CHE-1802789, CHE-2041108)
- 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
- COVID-19 pandemic, drug discovery, computer-aided drug design, drug repurposing, small molecule drugs, SARS-CoV-2, combination therapy, open science, clinical trials
- MeSH terms
- COVID-19, SARS-CoV-2, COVID-19 Drug Treatment, Antiviral Agents, Clinical Trials as Topic, Computer Simulation, Humans, Drug Design, Drug Discovery, Drug Repositioning, Pandemics
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