ArticleScientific reports2023
Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- VITAGRAPH: building a knowledge graph for biologically relevant learning tasks.Scientific data · 2026Article
- Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.Clinical proteomics · 2026Review
- Therapeutic monoclonal antibodies repurposing in oncology via IMGT/mAb-KG embeddings.BMC medical informatics and decision making · 2026Article
- BEACON: predicting side effects and therapeutics outcomes to drugs by Bridging knowlEdge grAph with CONtextual language model.bioRxiv : the preprint server for biology · 2026Article
- Knowledge graph embedding for predicting and analyzing microbial interactions.Scientific reports · 2025Article
- KGiA: Drug repurposing through disease-aware knowledge graph augmentation.Journal of biomedical informatics · 2025Article
- DrugReX: an explainable drug repurposing system powered by large language models and literature-based knowledge graph.Research square · 2025Article
- Knowledge Graphs for drug repurposing: a review of databases and methods.Briefings in bioinformatics · 2024Review
- Drug Repurposing using consilience of Knowledge Graph Completion methods.bioRxiv : the preprint server for biology · 2024Article
- Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph-Based Approach.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns "ensemble embeddings" of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.
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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 OpenQuestion graph.