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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]
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“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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Whisnant, A. W., & Dölken, L. (2026) · AI · doi:10.3390/ai7030093 · Cited by 3
The Therapeutic Potential of Novel Drug Targets in Metabolic Disorders- A Review
Mishra, R., Jakhar, M., Dhiman, R., Khan, L., Raj, A., Devi, V., Kumari, A., & Kumar, A. (2026) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/3333 · Cited by 2
CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction
Zhang, X., Jeong, E., Yan, C., Feng, Y., Lyu, L., Guo, X., & Chen, Y. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3819012 · Cited by 2
“For drug indication prediction, CellAwareGNN achieves an AUPRC of 0.826, representing a 1.2% improvement over TxGNN-U (0.816) and a 3.4% improvement over TxGNN (0.799).”
InfluentialRole of Technology in Detection of COVID-19
Lohiya, D. V., & Pathak, S. S. (2022) · Cureus · doi:10.7759/cureus.29138 · Cited by 2
“Artificial intelligence and network medicine technologies that are powerful, innovative and quickly developing can hasten the discovery of new treatments [31].”
BackgroundUtilizing clinical pharmacology in the drug repurposing arena: a look into COVID-19
Ruggiero, R., Balzano, N., Napoli, R. D., Sullo, M. G., Rossi, F., Capuano, A., & Rafaniello, C. (2022) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2022.2132226 · Cited by 2
“Baricitinib, dexamethasone, remdesivir, melatonin, mefuparib, and toremifene are some examples of potentially effective drugs against COVID-19 identified through AI [22].”
BackgroundHierarchical Vaccine Allocation Based on Epidemiological and Behavioral Considerations
Roy, S., Dutta, P., & Ghosh, P. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3265317 · Cited by 2
“The methods being employed pertain to diagnostics [8], drug discovery and repurposing [9], dynamic lockdowns [10], [11], [12], mobility scheduling [13], [14], [15], [16], etc.”
Drug Repurposing for COVID-19 using Computational Methods
Prakash, O., & Khan, F. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815123197123010010 · Cited by 2
Glebov, O. O., Mueller, C., Stewart, R. J., Aarsland, D., & Perera, G. (2023) · BMC Medicine · doi:10.1186/s12916-023-02877-9 · Cited by 2
Applications of Artificial Intelligence Methods in Medicine
Ivanović, M. D., & Maćoš, D. (2023) · doi:10.1109/icit58056.2023.10226107 · Cited by 3
“For example, in the paper [21] authors presented the importance of use of AI models in precision medicine i.”
Aktaş, M., & Çavuşoğlu, S. (2023) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub398.c1662 · Cited by 3
Charting the rise of ONNX in swift image processing: a publications exploration
Obaideen, K., Bonny, T., & Al-Shabi, M. A. (2024) · Real-Time Image Processing and Deep Learning · doi:10.1117/12.3013813 · Cited by 2
Debnath, R., Ikbal, A. M. A., Choudhury, A., Mandal, S. C., & Palit, P. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-1148-2_19 · Cited by 2
Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors
Pei, Q., Wu, L., He, Z., Zhu, J., Xia, Y., Xie, S., & Yan, R. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679704 · Cited by 2
“DTA measures the interaction strength between a drug and a target, and the accurate prediction can greatly benefit Virtual Screening (VS) [21] and expedite drug repurposing [41], e.g., finding potential drugs for COVID-19 [67].”
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
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