Cited by 636Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and EducationCOVID-19 diagnosis using AI

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Artificial intelligence in COVID-19 drug repurposing

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Zhou, Yadi, Wang, Fei, Tang, Jian, Nussinov, Ruth, Cheng, Feixiong·2020·The Lancet Digital Health

DOI 10.1016/s2589-7500(20)30192-8PMC PMC7500917

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Abstract

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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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
ORCID: Zhou, Yadi
Wang, Fei
Tang, Jian
Nussinov, Ruth
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)
DOI: 10.1016/s2589-7500(20)30192-8
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: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
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

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