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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Existing drug-repurposing AI models focus narrowly on diseases for which some drugs already exist, limiting their clinical utility.
“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]
The ability of existing machine learning models to identify therapeutic candidates for diseases with incomplete, sparse data and zero-known therapies drops drastically.
“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]
Geometric deep-learning models optimized on large medical KGs can match disease signatures to therapeutic candidates based on networks perturbed in disease.
“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]
The standard benchmarking strategy used to evaluate drug-repurposing AI models aligns with the approach predominantly used in the literature.
“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]
Graph representation learning has been successful in characterizing drugs at the systems level without patient data to predict interactions with other drugs, protein targets, side effects, and diseases.
“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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Self-citation
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- 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 · 2020: 17
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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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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.
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