ArticleProceedings of the National Academy of Sciences of the United States of America2021
Network medicine framework for identifying drug-repurposing opportunities for COVID-19.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 276 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
276 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review.Journal of biomolecular structure & dynamics · 2022Pooled it
- Psychotropic Drugs: A Promising Non-Antibiotic Agent to Combat Multi-Drug-Resistant Microorganisms.Drug development research · 2026Review
- TargetPrior: a miRNA-signature embedded evolutionary learning framework for prioritizing drug targets in acute myeloid leukemia.Bioinformatics (Oxford, England) · 2026Article
- Network pharmacology identifies repurposable drugs targeting host pathways across the oral-gut-lung axis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- From Chatbots to Co-Scientists: The Impact of Knowledge-Generating AI (AI 4.0) on Healthcare and Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Network-driven discovery of repurposable drugs targeting hallmarks of aging.Nature aging · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Mapping the network architecture of aging to identify repurposable drug candidates for longevity.Nature aging · 2026Article
- RepurNet: A network medicine framework for systematic drug repurposing and disease vulnerability discovery.iScience · 2026Article
- Policy barriers to drug repurposing in Europe: different stakeholder perspectives identified during survey-based shortlisting of key challenges.Health policy OPEN · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- KG-bench: benchmarking graph neural network algorithms for drug repurposing.Bioinformatics (Oxford, England) · 2026Article
- Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature biomedical engineering · 2026Article
- Precision Medicine Through Network Language: Integrating Clinical Insight and Data Expertise.Genes · 2026Review
- Drug repurposing against viral infections (2020-2025): clinical trials, computational strategies, and therapeutic interventions.Inflammopharmacology · 2026Review
- A dual-branch graph neural network architecture for drug-target binding affinity prediction.Scientific reports · 2026Article
- Community structure unveils the path multiplicity in complex networks.Nature communications · 2026Article
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
216 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.