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Artificial intelligence in COVID-19 drug repurposing
Open accessAbstract
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.
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“In some cases, these AI-powered approaches have been able to identify promising drug candidates in a fraction of the time that it would take when using traditional methods [26,27,28,29,30,31].”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [28]
In the citing paper
“ML algorithms have been used to analyze large datasets of potential compounds and identify those with the most potential for treating the virus.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [28]
In the citing paper
“The study is a strong proof for the use of AI-assisted tools for drugs and repayment for human diseases, including COVID-19 in times of fatal disease [11].”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [11]
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Metadata
- Authors
- Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F.
- Affiliations
- Cleveland Clinic (US)
- Cornell University (US)
- HEC Montréal (CA)
- Tel Aviv University (IL)
- Corresponding author
- Cheng, Feixiong
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 2
- Issue
- 12
- Pages
- e667-e676
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC7500917
- OpenAlex
- W3087156149
- Cited by
- 636
- References
- 89
- Citations by year
- 2026: 50 · 2025: 74 · 2024: 73 · 2023: 131 · 2022: 117 · 2021: 173 · 2020: 18
- FWCI
- 9.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- reponame:Expeditio Repositorio Institucional UJTL
- Funders
- National Science Foundation (2027970, 1750326, HHSN261200800001E)
- Cleveland Clinic
- Frederick National Laboratory for Cancer Research (HHSN261200800001E.)
- National Institutes of Health (#HHSN261200800001E, COVID-19, HHSN261200800001C)
- National Institute on Aging (R01AG066707)
- National Heart, Lung, and Blood Institute (R00HL138272, HHSN261200800001E)
- Office of Naval Research (N00014-18-1-2585, N00014)
- National Cancer Institute (HHSN261200800001E))
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- drug repurposing, COVID-19 pandemic, artificial intelligence, network medicine, precision medicine, drug target identification, drug development acceleration, big data
- MeSH terms
- SARS-CoV-2, COVID-19 Drug Treatment, Algorithms, Antiviral Agents, Artificial Intelligence, Drug Therapy, Combination, Humans, Treatment Outcome, Drug Discovery, Drug Repositioning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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