The DOI matches its Crossref record; 88 of the 89 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N.-H., Nitsche, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.052 · Cited by 20,286
Dexamethasone in Hospitalized Patients with Covid-19
The RECOVERY Collaborative Group (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2021436 · Cited by 11,477
Effect of Dexamethasone in Hospitalized Patients with COVID-19 – Preliminary Report
Horby, P., Lim, W. S., Emberson, J., Mafham, M., Bell, J., Linsell, L., Staplin, N., Brightling, C., Ustianowski, A., Elmahi, E., et al. (2020) · doi:10.1101/2020.06.22.20137273 · Cited by 11,475
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Remdesivir for the Treatment of Covid-19 — Final Report
Beigel, J. H., Tomashek, K. M., Dodd, L. E., Mehta, A. K., Zingman, B. S., Kalil, A. C., Hohmann, E., Chu, H. Y., Luetkemeyer, A., Kline, S., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2007764 · Cited by 7,267
Computing Machinery and Intelligence (1950)
Turing, A. M. (2004) · doi:10.1093/oso/9780198250791.003.0017 · Cited by 6,120
(2013) · Cited by 5,726
Cited by
Sorted by citations
Items 601–633 of 633
Sapa, H., Ayana, K., Prajitha, P. G., Shona, S. S., Althaf Umar, K. P., Kaladhar, K., Praveen, V. P., Vivek, N., K, P., Venugopal, B. S., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40441-2.00001-9
Toofanee, M. S. A. (2023) · theses.fr (ABES) · doi:10.70675/600337cazbfe4z405fz9538za725b88272b7
Pinero, S. L., Li, X., Liu, L., Li, J., Lee, S., & Le, T. D. (2026) · medRxiv · doi:10.64898/2026.04.12.26350701
Sun, B., Lu, Y., Liu, W., & Wang, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.28.721277
Artificial intelligence-powered pipelines for therapeutic innovation
Ali, S. A. M., Amrin, S. S., Naveed, M. A. U., & Mujahid, M. N. U. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27638-5.00011-0
AI-driven drug development: integrating computational design, 3D printing, and genomic medicine
Medhansh, D. M., Mohan, D., Barwa, R., PAINGINKAR, T., Karri, V. V. S. R., Osmani, R. A. M., Muddukrishna, B. S., & Ravi, G. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08748-1
Tian, L., Liu, S., Zhang, B., Hu, J., Zhou, Y., Hao, S., Li, J., Xin, L., Ke, C., Yang, Z., et al. (2026) · Fundamental Research · doi:10.1016/j.fmre.2026.04.028
AI-Driven Innovations in Pulmonary Medicine
Aminabee, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7759-9.ch011
From AI Agents to AI Scientists in Health: Emerging Landscape, Challenges, and the Path ForwardPreprint
Chen, Q., Wang, X., Zhou, R., Kim, H., Wang, S., Zhao, W., Liu, T., Riaz, I., He, L., Zhao, Y., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6889798 · Cited by 1
Jagannath, M., Sunderesan, A., Gupta, M. K., & Sudandiradoss, C. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1869543
Computational Pipelines for Disease-Specific Drug Repurposing
Abdelsayed, M. (2026) · Pharmaceutical science. · doi:10.5772/intechopen.1015541
MMTF-DTI: Drug–target interaction prediction via multimodal feature extraction and dynamic fusion
Zeng, P., Li, Z., Tang, X., Meng, Y., Zhang, Z., Cui, F., Jiang, J., & Xu, J. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105069 · Cited by 1
Zemnou, C. T., Ngakam, R., Tepap, S. S. D., Simo, F. B. N., Zanchi, F. B., Seukep, A. J., Kouam, A. F., & Huy, N. T. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111832
Loughrey, D. G., Brosens, C., Moiselle, R., Shterionov, D., Vandeghinste, V., Way, A., & Leeson, L. M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01230-z · Cited by 1
Artificial intelligence in molecular diagnostics for pandemic preparedness
Sagar, K., Mukherjee, D., Savareh, B. A., Touré, C. T., Ceruti, A., Ghosh, P., Weidmann, M., Truyen, U., Wahed, A. A. E., & Kobialka, R. M. (2026) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2026.2697900
“As a result, diagnostics can be updated faster and more precisely, improving disease detection and public health responses [34,35].”
AI for Science: AI-Aided Optimization and Implementation of Path-finding Algorithm
Wang, H., Wang, B., & Peng, Z. (2025) · International Conference on Intelligent Decision-making and Machine Learning (ICIDML) · doi:10.1109/icidml69845.2025.00011
Representational efficiency and noise robustness in hybrid quantum-classical graph neural networks
Santo, I. A., Bannah, H., Nabi, M. S., & Karim, H. B. A. (2026) · Discover Quantum Science · doi:10.1007/s44464-026-00025-9
“For example, Fang et al. [6] combined endophenotype-driven network medicine with real-world evidence to nominate sildenafil as an AD candidate, while Zhou et al. [7] demonstrated AI-guided frameworks that integrate multi-omics signals for systematic candidate discovery.”
Wen, J., Yu, X. M., & Yang, W. (2026) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-026-10917-z
“…including fully connected feedforward neural networks (FNN), convolu - tional neural networks (CNN), and recurrent neural net - works (RNN) [49 ], analyze diverse data sources, such as electronic health records, genomic studies, and compound structures, to identify potential therapeutic…”
Otaru, A. J., Abubakar, F. Z., & Ibitoye, M. O. (2026) · Respiratory Medicine · doi:10.1016/j.rmed.2026.109121
Sharma, P., Tiwari, S., Park, N., & Szeleszczuk, Ł. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27177864
“This novel approach reveals new therapeutic applications for existing medications, including those that are approved, archived, or undergoing clinical trials to treat various emerging diseases [24].”
Kulkarni, S., Joshi, M., Patil, R., & Deshmukh, A. (2022) · Journal of Health Informatics and Digital Systems · doi:10.68159/j927130731
Tanaka, H., Sato, Y., Mori, K., Okabe, R., & Ito, T. (2021) · Journal of Health Informatics and Digital Systems · doi:10.68159/d049294389
Huang, Y., Deng, Y., Tang, J., & Wang, J. (2026) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2026.119929
Raut, S., Gawande, S., lanjekar, R., & Mahabhudhe, J. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100625
Bai, L., Hu, F., Zhan, X., & Liu, C. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135201
Sun, X., Wan, X.-Y., Cao, S.-J., Gong, R., Jiang, S., Yi-zhi, L., Cheng, Y.-N., Yun-gui, L., Jiang, T., Xiang, Z.-Y., et al. (2026) · Acta Pharmacologica Sinica · doi:10.1038/s41401-026-01921-7
Kandati, D. R., Anusha, S., Ashalatha, N., & Ahmed, M. A. (2026) · International Journal of Clinical Microbiology and Biochemical Technology · doi:10.29328/journal.ijcmbt.1001042
Machine Learning-Guided Discovery of Potential Natural KRAS-G12D Inhibitors for Pancreatic Carcinoma
Alquraini, A., Alahmari, M., Alkholifi, F. K., Alghazwani, Y., Qadri, M., Alomar, F. A., Almarghalani, D. A., & Imran, M. (2026) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-026-11123-7
“This approach involves numerous computerized techniques such as docking, density functional theory, molecular dynamics simulation, binding energy calculation (MM/GBSA), and drug selection predictive modeling [ 15].”
Khullar, S., Sobhan, A., & Ali, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8562-4.ch001
BindingDB (2026) · UC San Diego · doi:10.7270/q2q52vj4
Tavakol, D. N., Mendonca, D., Baldassarri, I., Maletić‐Savatić, M., & Vunjak‐Novakovic, G. (2026) · Cell stem cell · doi:10.1016/j.stem.2026.10.003
Yao, J., Zhang, X., Liu, X., Liu, Z., Wang, F., Zhang, L., Yu, Y., Yan, C., & Xu, H. (2026) · Nanoscale Horizons · doi:10.1039/d6nh00326e
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 ›
Are you an author, or do you see a problem with this page? Request a review
