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

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 access

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

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

Open this paper in CoChat — free account Open PDF

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.

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

Crossref · 10.1016/s2589-7500(20)30192-8

Cites

88 of 88 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Deep learning
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
SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor
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
Untitled work
(2013) · Cited by 5,726

Show all 89 references

Sorted by citations

Items 401–500 of 633

Repurposing, effects, design, and discovery of drugs in systems biology
Amjad, E., & Sokouti, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22326-6.00024-9 · Cited by 2
Towards Improving the Efficiency of Drug Repurposing by Leveraging Node Promiscuity in Biomedical Knowledge Graphs
Korn, D., Hou, P.-Y., Schatz, K., Beasley, J.-M. T., Tropsha, A., & Chirkova, R. Y. (2024) · ACM Transactions on Computing for Healthcare · doi:10.1145/3707650 · Cited by 2
“For instance, rare diseases [78, 96] and COVID-19 [87, 92, 94, 95, 97] have been some of the recent popular targets of computational drug repurposing.”
Intersections between Information Science and Big Data
Perozo-Vasquez, J. G., Depizzolatti, A. T., & Werlang, N. B. (2025) · Em Questão · doi:10.1590/1808-5245.31.140613 · Cited by 2
Predictive for patients with pneumonia in pediatric intensive care unit
Jia, M., Hu, X., Lin, J., Lin, J., Liu, J., & Wang, Y. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1583573 · Cited by 2
System Theoretic Methods in Drug Discovery and Vaccine Formulation: Review and Perspectives
Sharma, A., Hsiao, Y.-C., & Dutta, A. (2025) · Drugs and Drug Candidates · doi:10.3390/ddc4030028 · Cited by 2
A value-based approach for training of classifiers with high-throughput small molecule screening data
Khuri, N., & Parsons, S. (2021) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3459930.3469514 · Cited by 2
“In recent years, both approaches, structure-based and ligandbased, have greatly benefited from an increased adoption of artificial intelligence (AI) methods [5, 18, 31].”
Background
springD2A: capturing uncertainty in disease–drug association prediction with model integration
Wang, W., Zhang, X.-W., & Dai, D. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab820 · Cited by 2
Intelligent Covid-19 Vaccine Supplychain Management System
Abdelgani, Y., Makki, Q. H., Ali, H. M., & AlZu’bi, S. M. F. (2022) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/icics55353.2022.9811172 · Cited by 2
“…and the epidemic affects everyone even those who have obtained different vaccines and the use of artificial intelligence(AI) techniques and deep learning (DL) [16] [17] [18] [19] [20] [21] [22] [23], due to the emergence of mutations which contributed to the weakening of the efficacy of vaccines.”
Background
AI for Drug Repurposing in the Pandemic Response
Truong, A. T. L., Blasiak, A., Egermark, M., & Ho, D. (2022) · Springer eBooks · doi:10.1007/978-3-031-08506-2_3 · Cited by 2
Repurposing Drugs for Viruses and Cancer: A Novel Drug Repositioning Strategy for COVID-19
Wong, K. H., Lim, C.-M., Yip, A. J. W., Farouk, I. A., Zabidi, N. Z., Low, Z. Y., & Lal, S. K. (2023) · Springer eBooks · doi:10.1007/978-981-19-5399-6_18 · Cited by 2
Applications of AI in Computer-Aided Drug Discovery
Kohli, R. K., Santoshi, S., Yadav, S. S., & Chauhan, V. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-5255-4.ch005 · Cited by 2
Future
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0003 · Cited by 2
AI and the UN Sustainable Development Goals
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0012 · Cited by 2
Estudo comparativo das produções científicas e tecnológicas dos especialistas seniores em Covid-19 após um ano de pandemia no Brasil
Chaves, H. K., da Silva, J. M. B., Hir, M. E. L. A., da Silveira, C. C. D. F., Mena‐Chalco, J. P., & de Magalhães, J. L. (2024) · Tempus Actas de Saúde Coletiva · doi:10.18569/tempus.v16i3.3123 · Cited by 2
A Review on Novel Coronavirus Outbreak: Current Scenario of Bangladesh
Khan, M. N. M., & Barman, N. C. (2020) · Open Access Macedonian Journal of Medical Sciences · doi:10.3889/oamjms.2020.5171 · Cited by 2
“Drug repurposing has become a promising approach for reducing the timelines of new drug development, cost, and making the treatment strategy easy [86].”
Background
Integrated Docking and Enhanced Sampling Based Selection of Repurposing Drugs for SARS-CoV-2 by Targeting Host Dependent FactorsPreprint
Kumawat, A., Namsani, S., Pramanik, D., Roy, S. K., & Singh, J. K. (2020) · ChemRxiv · doi:10.26434/chemrxiv.13369763 · Cited by 3
Graph Representation Learning for Covid-19 Drug Repurposing
Boutorh, A., Marref, K., & Dehiri, N. E. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-12097-8_6 · Cited by 1
An Empirical Investigation of the Demographic Differences in Attitude Toward Femvertising: The Case of Turkey
Yurcu, M. E. (2023) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub398.c1667 · Cited by 1
Pazarlama Araştırmalarında Yapay Zeka (AI) ile Müşteri Deneyimi: Tüketici Davranışlarının Analizinde Yapay Zeka Çözümleri
Atlı, H. F. (2023) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub398.c1663 · Cited by 1
Deneyimsel Pazarlama Kavramının Bibliyometrik Analiz ile İncelenmesi
Erdem, A. (2023) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub398.c1665 · Cited by 1
Advances in Network-Based Drug Repositioning
Pellegrini, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55248-9_5 · Cited by 1
A Systematic Review on Artificial Intelligence (AI) Assisted Drug and Vaccine Development against SARS-CoV-2
Singha, V., Ghosh, S., Singha, P. S., Datta, S., & Ghosh, D. (2024) · Coronaviruses · doi:10.2174/0126667975309811240530114325 · Cited by 1
Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery
Bhojwani, H. R., Rajnani, N., Hare, A., & Kurup, N. (2025) · Advances in pharmacology · doi:10.1016/bs.apha.2025.01.014 · Cited by 1
Artificial intelligence in GPCR drug discovery: A paradigm shift in computational pharmacology
Vinter, A., & Grgičević, I. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00047-9 · Cited by 1
Comparative analysis on artificial intelligence methods for DTI and DTBA prediction in drug repurposing
Kumar, S., & Singh, A. (2025) · Medicinal Chemistry Research · doi:10.1007/s00044-025-03465-7 · Cited by 1
“The ﬁ rst approach is based on deep learning and machine learning, which are able to create models of biological systems and reveal hidden patterns in large data sets [5].”
Pathways to precision medicine in cervical cancer: a comprehensive review of small molecule inhibitors, biomarkers and computational approaches
Purohit, N., Mishra, Y., Hazarika, P., Gopalakrishnan, A. V., & Murali, R. (2025) · 3 Biotech · doi:10.1007/s13205-025-04556-3 · Cited by 1
“AI algorithms create computers or programs that mimic human intelligence (Zhou et al. 2020).”
Biologically Informed Machine Learning Prioritizes Dietary Supplements That Protect Neural Crest Cells from Ethanol-Induced Epigenetic Dysregulation and Developmental Impairment
Wang, X., Bai, M., Wang, S., Qian, H., Liu, J., Feng, W., Zhang, H., Wu, X., & Chen, S.-Y. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms27010295 · Cited by 1
DDA-bench: a manually curated database for benchmarking datasets and baseline performance values in predicting drug-disease associations
Xing, H., & Zhao, Y. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2025.1755600 · Cited by 1
Digital Revolution in Pharmaceutical Industry: A Network Analysis of Industry 5.0 Technologies
ERİM, M. M., D’Alessandro, C., Szopik‐Depczyńska, K., & Ioppolo, G. (2026) · Springer proceedings in complexity · doi:10.1007/978-3-032-09066-9_25 · Cited by 1
AI technologies for smart pharmacology
Vișan, A. I., & Neguț, I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00003-8 · Cited by 1
Circulating and Respiratory Biomarkers in Sepsis-Induced ARDS: Diagnostic and Prognostic Insights – A Narrative Review
Muhoza, B.-G., liu, S., Niyonkuru, E., & Sun, T. (2026) · Journal of Inflammation Research · doi:10.2147/jir.s571504 · Cited by 1
Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era
Nasr, D. S., AlRaee, N. B., Katbi, S. W. A., Kouli, N. M., Asad, N. I., Ismail, M. M., & Khanday, S. (2026) · New Microbes and New Infections · doi:10.1016/j.nmni.2026.101751 · Cited by 1
Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides
Zhou, Q., Sun, L., Ou, Y., Liu, H., Gao, J., Dai, R., Li, Y., Cheng, G., Tao, Y., Pan, Y., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag107 · Cited by 1
“Machine learning (ML) and deep learning (DL) algorithms, such as protein structure prediction [12–14], virtual screening [15–19], toxicity prediction [20–22], pharmacophore modeling [23–26], drug r epositioning [27–34], and clinical monitoring [35–46], have been applied to drug discovery.”
DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery
Zhang, Q., Yu, X., Wei, Y., Xia, Y., Shen, L.-C., Wang, Z., Shen, H., & Yu, D. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag392 · Cited by 1
“During the life cycle of SARS-CoV-2, the 3CL protease is a crucial part of virus replication (Zhou et al. 2020).”
Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions
Shi, Q., Xuan, M., Lv, C., Zhang, Z., Geng, X., Huang, D., & Hu, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1776472 · Cited by 1
Drug Repurposing and Dosage Form Development of Anti-COVID-19
Kwok, K. (2021) · Pharmaceutical Sciences and Research · doi:10.7454/psr.v8i1.1200 · Cited by 1
“In addition, drug repurposing can also reduce the costs required in its process and has lesser risk in the research (Agrawal, 2015; Parvathaneni et al., 2019; Zhou et al., 2020).”
Background
Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease
Seo, Y., Jang, H., & Lee, H. (2022) · Life · doi:10.3390/life12020275 · Cited by 1
Toward the Analysis of Graph Neural Networks
Nguyen, T.-D., Le-Cong, T., Nguyen, T. H., Le, X.-B. D., & Thắng, H. Q. (2022) · IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) · doi:10.1109/icse-nier55298.2022.9793533 · Cited by 1
Towards Network Medicine: Implementation of Panomics and Artificial Intelligence for Precision Medicine
Barber, R. D., & Kroeger, K. (2022) · Healthcare delivery in the information age · doi:10.1007/978-3-030-95675-2_3 · Cited by 1
A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence
Sharma, V., Dastidar, M. G., Sutradhar, S., Raj, V., Silva, K. D., & Roy, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91307-2.00017-1 · Cited by 2
“The Emeraldwas produced by a group headed byMIT scientist Dina Katabi.(95) It includes a WiFi-like box that detects wire-”
A novel logical model of COVID-19 intracellular infection to support therapies development
Piretto, E., Selvaggio, G., Bragantini, D., Domenici, E., & Marchetti, L. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010443 · Cited by 1
Computing New Medicines
DeFranco, J. F., & Voas, J. M. (2023) · Computer · doi:10.1109/mc.2022.3191933 · Cited by 1
The World Needs a “Pandamic” Solution for a Pandemic Problem
Huang, J., Döring, O., & Liu, G. G. (2022) · China CDC Weekly · doi:10.46234/ccdcw2022.238 · Cited by 1
Active regression model for clinical grading of COVID-19
Sh, Y., Dong, J., Chen, Z., Yuan, M., Lyu, L., & Zhang, X.-L. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1141996 · Cited by 1
New targets for old drugs
Kumbhar, N., Aparna, M. A., Nimal, S. K., Shewale, P., Barale, S. S., & Gacche, R. N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15232-0.00021-7 · Cited by 1
Soft Ethics and the Governance of AI
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0006 · Cited by 1
Tedarik Zinciri Bakış Açısıyla Şirketlerin Yeşil Aklama ile Başa Çıkma Stratejileri
Sofyalıoğlu, Ç., & Kartal, B. (2023) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub398.c1661 · Cited by 1
An interdisciplinary course on computer‐aided drug discovery to broaden student participation in original scientific research
Stratton, C. F., Christensen, A., Jordan, C., Salvatore, B. A., & Mahdavian, E. (2024) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.21811 · Cited by 1
“25,26 CADD is also highly amenable to repurposing drugs that can serve as seeds for new targets associated with a new disease, thereby leveraging an existing drug's established safety profile.”
Background
Molecular Dynamics Simulation of SARS-CoV-2 E Ion Channel: The Study of Lone Protein and its Conformational Changes in Complex with Potential Cage Inhibitors
Shiryaev, V. A., Ivleva, E. A., Zaborskaya, M. S., Tkachenko, I. M., Osyanin, V. A., & Klimochkin, Y. N. (2024) · Current Computer - Aided Drug Design · doi:10.2174/0115734099247899240326073802 · Cited by 1
NLP Applications—Biomedical Literature
Zhang, R., Kastrin, A., Hristovski, D., Marcelo Fiszman, B., & Halil Kilicoglu, B. (2024) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-55865-8_13 · Cited by 1
A phenotypic drug discovery approach by latent interaction in deep learning
Yu, B. T. W. (2024) · Royal Society Open Science · doi:10.1098/rsos.240720
“This is a common limitation of the deep learning approach [84].”
Artificial intelligence: Blessing or curse?
Briganti, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23655-6.00005-8 · Cited by 1
Reflections on “Can AI Understand Our Universe?”
Wang, Y. (2024) · International Journal of Modern Physics D · doi:10.1142/s0218271825400103 · Cited by 1
Revolutionizing Drug Development: Harnessing Artificial Intelligence in Pharmaceutical Sciences
Roy, S. P., Kadiri, S. K., Bhowmik, S., Patel, V., Deb, L., & Tiwari, P. K. (2025) · Current Drug Discovery Technologies · doi:10.2174/0115701638343448250207053913 · Cited by 2
Coronavirus Disease 19 and Future Ecological Crises: Hopes from Epigenomics and Unraveling Genome Regulation in Humans and Infectious Agents
Dzobo, K. (2021) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2021.0024 · Cited by 1
“Reports indicate that consideration of the age factor during treatment of COVID-19 patients is reducing mortality in many countries (Cascella et al., 2020; Colaneri et al., 2020; Liu et al., 2020b; Vijayvargiya et al., 2020; Zhou et al., 2020a).”
Computational Methods and Tools for Repurposing of Drugs Against Coronaviruses
Chakraborti, S., Bheemireddy, S., & Srinivasan, N. (2021) · Methods in pharmacology and toxicology · doi:10.1007/7653_2020_60 · Cited by 1
Informetric analysis of researches on application of artificial intelligence in COVID-19 prevention and control
Liu, Z., Chen, S., & Han, Q. (2021) · International Conference on Image, Video Processing and Artificial Intelligence (IVPAI) · doi:10.1117/12.2612150 · Cited by 1
“’s research found that, through the big data analysis of pathological research results and various cases, AI can find the advantages and disadvantages of various treatment plans and the therapeutic effects of various drugs, and even provide drug combination plans for the treatment[8].”
Background
Improving New Zealand Health Outcomes with Artificial Intelligence
Sehgal, T., & Cherrington, M. (2021) · Scope Contemporary Research Topics (Health and Wellbeing) · doi:10.34074/scop.3006005 · Cited by 1
Internet of Health Things (IoHT): The Significance of Virtual Tools Aiding to Overcome Novel Coronavirus (COVID-19) Pandemic
Gunturu, L. N., & Dornadula, G. (2022) · Disruptive technologies and digital transformations for society 5.0 · doi:10.1007/978-981-16-3783-4_2 · Cited by 1
Artificial intelligence methods to repurpose and discover new drugs to fight the Coronavirus disease-2019 pandemic
Santana, M. V., & Silva, F. P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91172-6.00016-9 · Cited by 1
“In reinforcement learning, the model or agent is trained to maximize a reward function; for example, to generate molecules with better binding affinity on a protein target (Blaschke et al., n.d.; Olivecrona et al., 2017; Zhou et al., 2020).”
Repurposing Molnupiravir as a new opportunity to treat COVID-19
Sahithi, P., Yetukuri, K., & Nadendla, R. (2022) · Journal of Generic Medicines The Business Journal for the Generic Medicines Sector · doi:10.1177/17411343221115819 · Cited by 1
“Indeed, as the number of repositioned pharmaceuticals mounted, it was suggested that a comprehensive screening of all known drugs would reveal new suitable drugs.(13)”
Background
Biotechnological Strategies in the Intervention and Treatment of COVID-19
Silva-Beltrán, N. P., Balderrama-Carmona, A. P., Gálvez-Ruíz, J.-C., Umsza‐Guez, M. A., & Ruíz-Bustos, E. (2022) · Springer eBooks · doi:10.1007/978-3-031-08045-6_21 · Cited by 1
Heterogeneous Graph based Deep Learning for Biomedical Network Link PredictionPreprint
Guo, J., Li, J., Leng, D., & Pan, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01649 · Cited by 2
“In these biomedicine networks, link prediction [3, 4] is one of the important network-based computing and modelling approaches for studying biomedicine relationships, which has great potency in drug repurposing [5], target identiﬁcation [6] and biomarker discovery [7].”
Mitigating losses: How science diplomacy can address the impact of COVID-19 on early career researchersPreprint
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) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/f9tsw · Cited by 1
The Application of Artificial Intelligence in Healthcare-Resource Allocation Before and During the COVID Pandemic: Scoping Review (Preprint)Preprint
Wu, H., Lu, X., & Wang, H. (2022) · doi:10.2196/preprints.38397 · Cited by 1
A Hybrid AI-driven Algorithm to Uncover Potential Therapeutic Targets for COVID-19 Using Network-based Drug RepurposingPreprint
Marchi, C., & Martins, D. (2022) · Preprints.org · doi:10.20944/preprints202207.0004.v1
Identification and Development of Therapeutics for COVID-19.Preprint
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) · PubMed · Cited by 1
DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learningPreprint
Saberian, M., Moriarty, K., Olmstead, A. D., Hallgrimson, C., Jean, F., Nabi, I. R., Libbrecht, M. W., & Hamarneh, G. (2022) · doi:10.36227/techrxiv.19326665 · Cited by 2
Trend and co-occurrence network study of symptoms through social media: an example of COVID-19Preprint
Wu, J., Wang, L., Hua, Y., Li, M., Zhou, L., Bates, D. W., & Yang, J. (2022) · medRxiv · doi:10.1101/2022.09.28.22280462 · Cited by 1
Copyright Page
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.002.0003 · Cited by 1
Editorial: All good things come to an end
(2023) · Physiology News · doi:10.36866/pn.132.6 · Cited by 1
WITHDRAWN: Antibiotic Bacteria Interaction: Dataset and BenchmarkingPreprint
Chatterjee, S., Majumdar, A., & Chouzenoux, É. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.22.581405 · Cited by 1
DREAM-GNN: Dual-route embedding-aware graph neural networks for drug repositioningPreprint
Zhao, Y., Chen, Y., Du, J., Sun, Q., Wang, R., & Chen, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.07.663530 · Cited by 1
SUPORTE PARA MÓDULO FOTOVOLTAICO COM INCLINAÇÃO VARIÁVEL
Costa, F. D. M., Bragante, R. A., Faesarella, A. S., & Coelho, F. A. (2021) · doi:10.22533/at.ed.9982113047
DESENVOLVIMENTO DE BIOPOLÍMERO A PARTIR DO AMIDO DE CHUCHU E AVALIAÇÃO DA INCORPORAÇÃO DO RESÍDUO DE CAFÉ E ÓLEO DE BURITI
da Costa, F. A. T., Júnior, J. P., & Baú, R. Z. (2021) · doi:10.22533/at.ed.99821130411
CELULOSE NANOFIBRILADA: ESTUDO DA OBTENÇÃO E APLICAÇÃO NA INDÚSTRIA PAPELEIRA
Zenni, M. R., dos Santos, C. P., & Bianchi, R. M. D. C. (2021) · doi:10.22533/at.ed.99821130410
MINIMIZAÇÃO DE SOBRECARGA ESTRUTURAL NA BLINDAGEM DA RADIOATIVIDADE
Beati, A. A. G. F., Campos, H. B., Brandão, A. A., & da Silva, N. R. (2021) · doi:10.22533/at.ed.99821130417
RASTREX – SISTEMA DE RASTREAMENTO VEICULAR
Matukava, S. H., Perassolli, V. S., Sablón, V. I. B., & Faesarella, A. S. (2021) · doi:10.22533/at.ed.9982113044
PRODUÇÃO DE COSMECÊUTICOS COM ÓLEO DE CAFÉ PARA PREVENÇÃO DO FOTOENVELHECIMENTO
Mariano, V. C. D. B., Lopes, N. C. D. B., & Tescarollo, I. L. (2021) · doi:10.22533/at.ed.99821130413
MACHINE LEARNING PARA DELINEAMENTO EXPERIMENTAL EM ESTUDOS DA DOR - IOT, REDE NEURAL, K-MEANS E ÁRVORE DE DECISÃO
Andrijauskas, F., Catroli, G. F., Horibe, E. K., Tralli, M. G., Torres, R. S., & Santos, J. M. (2021) · doi:10.22533/at.ed.9982113043
CASCA DE BANANA COMO BIOADSORVEDOR DE PIGMENTOS DE MEIO AQUOSO
Rodrigues, G., Gabriela, B., & Felippe, M. T. S. D. (2021) · doi:10.22533/at.ed.99821130416
GERADOR DE ENERGIA PIEZOELÉTRICO: AQUISIÇÃO, MONITORAMENTO E CONDICIONAMENTO DO SINAL GERADO
Antunes, D. F. D. D., & Sablón, V. I. B. (2021) · doi:10.22533/at.ed.9982113046
SMLP - SISTEMA DE MONITORAMENTO DE LINHA DE PRODUÇÃO
Lima, I. V., Jarussi, K. F., Faesarella, A. S., & Sablón, V. I. B. (2021) · doi:10.22533/at.ed.99821130414
MODELAGEM E SIMULAÇÃO DO PROCESSO DE FERMENTAÇÃO CONTÍNUA EM MICRO BIORREATOR
Godoy, J. P. F., Silva, G. B., & Coelho, F. A. (2021) · doi:10.22533/at.ed.9982113049
AMBIENTE DE APRENDIZADO PARA ESTUDO DE MÁQUINAS VIRTUAIS EM SISTEMA EMBARCADO
Oliveira, R. R., dos Santos, R. F., Catroli, G. F., & Andrijauskas, F. (2021) · doi:10.22533/at.ed.9982113045
THE IMPACT OF COMPUTATIONAL INTELLIGENCE FOR COVID-19 AS A TECHNOLOGICAL RESOURCE TO SUPPORT THE GLOBAL PANDEMIC
França, R. P., Monteiro, A. C. B., Arthur, R., Segatti, A. C., Sablón, V. I. B., & Iano, Y. (2021) · doi:10.22533/at.ed.9982113042
VIABILIZAÇÃO DO USO DE MANUFATURA ADITIVA NOS PROCESSOS DE AGITAÇÃO E MISTURA
da Silva, T. H. A., Veris, M. B., & D’Amelio, M. T. S. (2021) · doi:10.22533/at.ed.9982113048
The Prediction of the Lifetime of the COVID-19 Virus using Parameter-dependent New Mathematical ModelsPreprint
Selvakumar, K. (2021) · Research Square · doi:10.21203/rs.3.rs-501782/v1
COVID-19 Global Pandemic Fight by Drugs: A Mini-Review on Hope and Hype
Tekale, S. U., Gore, V., Kendrekar, P. S., Thore, S. N., Kótai, L., & Pawar, R. (2021) · Mini-Reviews in Organic Chemistry · doi:10.2174/1570193x18666210629103117
Application of AI in Diagnosing and Drug Repurposing in COVID 19
Ravikumar, G. K., Bharadwaj, S. S., Niveditha, N. M., & Narendra, B. K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80624-8_15
CIGB-300 Synthetic Peptide, an Antagonist of CK2 Kinase Activity, as a Treatment for Covid-19. A Computational Biology ApproachPreprint
Miranda, J., Bringas, R., Fernández-de-Cossío, J., & Perera, Y. (2021) · Research Square · doi:10.21203/rs.3.rs-795492/v1
What Is Deep Learning and How Has It Helped the COVID-19 Pandemic?
Kartal, E., & Schwartz, O. (2021) · Advances in human services and public health (AHSPH) book series · doi:10.4018/978-1-7998-8674-7.ch018
Could repurposing existing vaccines and antibiotics help to control the COVID-19 pandemic?
Rathod, K., Dhingra, N., Dakshinamurthy, S., & Viswanath, B. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85662-1.00020-3
A review of emerging innovations in COVID-19
Saggi, S. (2021) · International Journal of Community Medicine and Public Health · doi:10.18203/2394-6040.ijcmph20214808
“A COVID-19 host genetics initiative is underway for data generation, sharing and analysis to identify the genetic determinants of COVID-19 susceptibility, severity and outcomes and personalised treatment.(10)”
Background
A Comprehensive Review: Prevention and Control of SARS Co-V-2
Chauhan, P., Katiyar, P., Kalpana, K., Verma, S. K., Parveen, A., & Yadav, R. K. (2022) · Acta Scientific Pharmaceutical Sciences · doi:10.31080/asps.2022.06.0835
Компьютерное моделирование лиганд-рецепторных взаимодействий маннозного рецептора CD206 в сравнении с модельным лектином конканавалином А
Zlotnikov, I. D., & Кудряшова, Е. В. (2022) · Biochemistry (Moscow) · doi:10.31857/s0320972522010055
A Systematic Biomedical Perspective on COVID-19 Pandemic: From Detection to Vaccination
Mir, J. M., Wani, R. A., John, H., Kambay, E. A., Malik, B. A., Majid, S. A., & Shalla, A. H. (2021) · Advanced Chemicobiology Research · doi:10.37256/acbr.1120211146
“Several shreds of evidence indicate NSAID’s role for COVID-19 treatment, however, in respect to this viral strain and the mutations that go on in the virus, it needs to be controlled under high order precautions [6].”
Background
Probióticos – uma espada ou um escudo no desfecho da COVID-19?
de Abreu, J. A. C., de Freitas, N. L., AZEVEDO, P. R. G., & Brandão, F. (2022) · Research Society and Development · doi:10.33448/rsd-v11i4.27165
AI based strategies for Covid-19 drug repurposing.Preprint
Srivastava, S., Preeti, P., Sharma, T., Goel, S., Rajput, K. G., & Rawal, K. (2022) · doi:10.31219/osf.io/c2u4x
The Role of Artificial Intelligence and Data Science Against COVID-19
Tripathi, S., & Sharma, D. K. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-0284-0_10

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

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

Artificial intelligence in COVID-19 drug repurposing

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks