The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
Network medicine framework for identifying drug-repurposing opportunities for COVID-19
Open accessAbstract
Significance: The COVID-19 pandemic has highlighted the importance of prioritizing approved drugs to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We experimentally screened 918 drugs, allowing us to evaluate the performance of the existing drug-repurposing methodologies, and used a consensus algorithm to increase the accuracy of the predictions. Finally, we screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19. The developed strategy has significance beyond COVID-19, allowing us to identify drug-repurposing candidates for neglected diseases.
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In the citing paper
“This evaluation method aligns with the approach predominantly used in the literature12,14–16,18–20.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [16]
Experimental — accuracy not yet measuredIn the citing paper
“The clinical utility of drug-repurposing artificial intelligence (AI) models remains limited because these models focus narrowly on diseases for which some drugs already exist.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [16]
In the citing paper
“Unfortunately, the ability of existing machine learning models to identify therapeutic candidates for diseases with incomplete, sparse data and zero-known therapies drops drastically16,22 (as we demonstrate across eight benchmarks in Fig. 2c,d).”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [16]
In the citing paper
“To provide therapeutic predictions, geometric deep-learning models optimized on large medical KGs15 can match disease signatures to therapeutic candidates based on networks perturbed in disease15–18.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [16]
In the citing paper
“Graph representation learning has been successful in characterizing drugs at the systems level without patient data to make predictions about interactions with other drugs, protein targets, side effects, diseases [6, 38, 39, 40, 48, 165].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [6]
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Citations
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Cited by
Sorted by citations
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
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
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823
Artificial intelligence in COVID-19 drug repurposing
Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30192-8 · Cited by 636
Building a knowledge graph to enable precision medicine
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Self-citation
Metadata
- Authors
- Morselli Gysi, D., do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S. D., Patten, J. J., Davey, R. A., Loscalzo, J., & Barabási, A.-L.
- Affiliations
- Network Science Institute, Northeastern University, Boston, MA 02115;
- Department of Biomedical Informatics, Harvard University, Boston, MA 02115;
- Northeastern University (US)
- Brigham and Women's Hospital (US)
- Data Science Department, Scipher Medicine, Waltham, MA 02453;
- Boston University (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-04-27
- Volume
- 118
- Issue
- 19
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W3017271475
- Cited by
- 107
- References
- 100
- Citations by year
- 2025: 5 · 2024: 6 · 2023: 16 · 2022: 15 · 2021: 47
- FWCI
- 11.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Europe PMC (PubMed Central) (submittedVersion, cc-by-nc-sa)
- Sabanci University (submittedVersion)
- Open Research (Auckland University of Technology) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- American Heart Association
- National Institutes of Health (HL 108630)
- National Heart, Lung, and Blood Institute (P01HL132825, R01HL155096, U54HL119145, U01HL108630, R01HL155107)
- National Human Genome Research Institute (U01HG007690)
- National Institute of Allergy and Infectious Diseases (R01AI128364, R01AI125453, P01AI120943, UC7AI095321)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- COVID-19 pandemic, SARS-CoV-2, drug repurposing, network medicine, drug prioritization, network diffusion, network proximity, consensus forecasts, antiviral drug discovery, neglected diseases
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