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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Prosociality in Cyberspace: Developing Emotion and Behavioral Regulation to Decrease Aggressive Communication
Simão, A. M. V., Ferreira, P. C., Pereira, N., Oliveira, S., Paulino, P., Rosa, H., Ribeiro, R., Coheur, L., Carvalho, J. P., & Trancoso, I. M. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09852-7 · Cited by 26
Artificial intelligence applications used in the clinical response to COVID-19: A scoping review
Mann, S., Berdahl, C. T., Baker, L., & Girosi, F. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000132 · Cited by 24
“A fuller accounting of AI in the pandemic response would also consider applications in public health [11], vaccine development [53], drug repurposing [54], and viral protein mapping [55].”
Background
Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations
Wang, R., & Loscalzo, J. (2023) · Circulation Research · doi:10.1161/circresaha.122.321879 · Cited by 23
Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer
Ahmed, F., Samantasinghar, A., Ali, W., & Choi, K. H. (2024) · Molecular Diversity · doi:10.1007/s11030-023-10784-7 · Cited by 25
“A study in [21] categorized drug-repurposing techniques according to their primary source of biological data and fundamental methodology, such as molecular interaction networks, gene regulatory networks, and metabolic networks.”
Drug Repurposing in the Chemotherapy of Infectious Diseases
Hamid, A., Mäser, P., & Mahmoud, A. B. (2024) · Molecules · doi:10.3390/molecules29030635 · Cited by 22
“Regarding drug repurposing, AI-based tools have been utilized to accelerate drug discovery in the COVID-19 pandemic [172,173].”
Method
Exploring the future of biopharmaceutical drug discovery: can advanced AI platforms overcome current challenges?
Bettanti, A., Beccari, A. R., & Biccarino, M. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00188-3 · Cited by 28
“However, AI has the potential to dramatically increase the likelihood of clinical success with repurposed drugs, with the notable examples of treatments for COVID-19 [31, 32] and potentially cancer [33].”
Drug repurposing strategies and key challenges for COVID-19 management
Mule, S., Singh, A., Greish, K. F., Sahebkar, A., Kesharwani, P., & Shukla, R. (2021) · Journal of drug targeting · doi:10.1080/1061186x.2021.2013852 · Cited by 22
AI-powered drug repurposing for developing COVID-19 treatments
Liu, Z., Chen, X., Carter, W., Moruf, A., Komatsu, T. E., Pahwa, S., Chan‐Tack, K. M., Snyder, K., Petrick, N., Cha, K., et al. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824010-6.00005-8 · Cited by 26
Artificial intelligence streamlines scientific discovery of drug–target interactions
Yang, Y., & Cheng, F. (2025) · British Journal of Pharmacology · doi:10.1111/bph.17427 · Cited by 22
Enhancement of the IFN-β-induced host signature informs repurposed drugs for COVID-19
Huang, C.-T., Chao, T., Kao, H., Pang, Y.-H., Lee, W.-H., Hsieh, C.-H., Chang, S., Huang, H., & Juan, H. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05646 · Cited by 20
Pharmacoinformatics approach based identification of potential Nsp15 endoribonuclease modulators for SARS-CoV-2 inhibition
Savale, R. U., Bhowmick, S., Osman, S. M., Alasmary, F. A., Almutairi, T. M., Abdullah, D. S., Patil, P. C., & Islam, M. A. (2021) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2021.108771 · Cited by 22
Bioeconomy during the COVID-19 and perspectives for the post-pandemic world: Example from EU
Woźniak, E., & Tyczewska, A. (2021) · EFB Bioeconomy Journal · doi:10.1016/j.bioeco.2021.100013 · Cited by 20
“Since the beginning of the pandemic, there has been an intense global ffort to repurpose existing approved drugs for the treatment of COVID9 ( De Savi, 2020 ; Zhou et al., 2020 ; Delre et al., 2020 ; Zhang et al., 021 ; Singh et al., 2020 ; Lobo-Galo et al., 2021 ).”
Background
Mitigating losses: how scientific organisations can help address the impact of the COVID-19 pandemic on early-career researchers
López‐Vergès, S., Urbani, B., Rivas, D. F., Kaur‐Ghumaan, S., Coussens, A. K., Moronta-Barrios, F., Bhattarai, S., Niamir, L., Siciliano, V., Molnar, A., et al. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-021-00944-1 · Cited by 21
Exploring the Role of Artificial Intelligence and Machine Learning in Pharmaceutical Formulation Design
Dey, H., Arya, N., Mathur, H., Chatterjee, N., & Jadon, R. (2024) · International Journal of Newgen Research in Pharmacy & Healthcare · doi:10.61554/ijnrph.v2i1.2024.67 · Cited by 26
DrugMAP 2.0: molecular atlas and pharma-information of all drugs
Li, F., Mou, M., Xiao-yi, L., Xu, W., Yin, J., Zhang, Y., & Zhu, F. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae791 · Cited by 20
Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates
Serafim, M. S. M., Gertrudes, J. C., Costa, D. M. A., Oliveira, P. R., Maltarollo, V. G., & Honório, K. M. (2021) · Bioscience Reports · doi:10.1042/bsr20202616 · Cited by 22
Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug–Gene Interactions
Groza, V., Udrescu, M., Bozdog, A., & Udrescu, L. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13122117 · Cited by 20
Development and Trends in Artificial Intelligence in Critical Care Medicine: A Bibliometric Analysis of Related Research over the Period of 2010–2021
Cui, X., Chang, Y., Yang, C., Cong, Z., Wang, B., & Leng, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010050 · Cited by 19
Holistic Approach for Artificial Intelligence Implementation in Pharmaceutical Products Lifecycle: A Meta-Analysis
Koshechkin, K. A., Lebedev, G. S., Fartushniy, E., & Orlov, Y. L. (2022) · Applied Sciences · doi:10.3390/app12168373 · Cited by 21
Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study
Wu, J., Wang, L., Hua, Y., Li, M., Zhou, L., Bates, D. W., & Yang, J. (2023) · Journal of Medical Internet Research · doi:10.2196/45419 · Cited by 20
Current and emerging prospects in the psoriatic treatment
Nikam, R. V., Gowtham, M., More, P. S., & Shinde, A. S. (2023) · International Immunopharmacology · doi:10.1016/j.intimp.2023.110331 · Cited by 19
Artificial Intelligence Applications in Healthcare
Usmani, U. A., Happonen, A., Watada, J., & Khakurel, J. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3091-3_89 · Cited by 23
A hierarchical convolutional model for biomedical relation extraction
Hu, Y., Chen, Y., Huang, R., Qin, Y., & Zheng, Q. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103560 · Cited by 21
COVID term: a bilingual terminology for COVID-19
Ma, H., Shen, L., Sun, H., Xu, Z., Hou, L., Wu, S., Fang, A., Li, J., & Qian, Q. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01593-9 · Cited by 19
Artificial Intelligence and Precision Medicine: A Perspective
Lorkowski, J., Kolaszyńska, O., & Pokorski, M. (2021) · Advances in experimental medicine and biology · doi:10.1007/5584_2021_652 · Cited by 23
Role of Artificial Intelligence and Machine Learning in Bioinformatics: Drug Discovery and Drug RepurposingPreprint
Quazi, S. (2021) · Preprints.org · doi:10.20944/preprints202105.0346.v1 · Cited by 19
“The cutoff value was obtained from ANNOVAR, different databases and original studies [23, 24].”
Method
Identification and Development of Therapeutics for COVID-19
Rando, H. M., Wellhausen, N., Ghosh, S., Lee, A. J., Dattoli, A. A., Hu, F., Byrd, J. B., Rafizadeh, D. N., Lordan, R., Qi, Y., et al. (2021) · mSystems · doi:10.1128/msystems.00233-21 · Cited by 25
Artificial intelligence unifies knowledge and actions in drug repositioning
Yin, Z., & Wong, S. T. C. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210223 · Cited by 19
Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review
Deshpande, N. M., Gite, S., Pradhan, B., & Assiri, M. E. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.021225 · Cited by 22
AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine
Quan, X., Cai, W., Xi, C., Wang, C., & Yan, L. (2023) · Database · doi:10.1093/database/baad006 · Cited by 18
A computational study of potential therapeutics for COVID-19 invoking conceptual density functional theory
Saloni, S., Kumari, D., Ranjan, P., & Chakraborty, T. (2022) · Structural Chemistry · doi:10.1007/s11224-022-02048-1 · Cited by 16
Artificial Intelligence: A Century-Old Story
van Assen, M., Muscogiuri, E., Tessarin, G., & Cecco, C. N. D. (2022) · Contemporary medical imaging · doi:10.1007/978-3-030-92087-6_1 · Cited by 22
DRaW: prediction of COVID-19 antivirals by deep learning—an objection on using matrix factorization
Hashemi, S. M., Zabihian, A., Hooshmand, M., Gharaghani, S., & Shahbazi, M.-A. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05181-8 · Cited by 17
Effectiveness of Vaccines and Antiviral Drugs in Preventing Severe and Fatal COVID-19, Hong Kong
Cheung, Y. Y. H., Lau, E. H. Y., Yin, G., Lin, Y., Cowling, B. J., & Lam, K. F. (2023) · Emerging infectious diseases · doi:10.3201/eid3001.230414 · Cited by 18

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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