ArticleScientific reports2024
Explainable drug repurposing via path based knowledge graph completion.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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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
13 citing papers in PubMed.
- CAREPath: semantic context-aware reasoning paths with mechanism-augmented embeddings for drug repurposing.Briefings in bioinformatics · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Generating explainable hypotheses for drug repurposing with graph neural networks.Scientific reports · 2026Article
- Knowledge-graph embeddings for osteoarthritis candidate prediction.NPJ digital medicine · 2026Article
- Comic: explainable drug repurposing via contrastive masking for interpretable connections.BMC bioinformatics · 2026Article
- TRACE-DDI: A Hybrid Framework of Transformer-GAT Context Encoder and Pathway-Anchored Knowledge Graphs for DDI Prediction.Computational and structural biotechnology journal · 2026Article
- From benchmark accuracy to pharmacological credibility: why mechanistic explainability must define the next generation of AI for drug repurposing.Frontiers in pharmacology · 2026Article
- Drug Discovery from Gene Expression/Gene Regulation.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Mechanism-aware and safety-validated AI for drug repurposing: a critical review and translational pharmacology framework.Frontiers in pharmacology · 2026Review
- molIEreVIS: exploring and interpreting the evidence behind drug repurposing predictions.Frontiers in bioinformatics · 2026Article
- Knowledge Graphs for Cervical Cancer Across the Care Continuum: From Vaccination and Screening to Personalized Therapy.Therapeutics and clinical risk management · 2026Review
- Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning.Journal of translational medicine · 2025Article
- Universal multilayer network embedding reveals a causal link between GABA neurotransmitter and cancer.BMC bioinformatics · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Drug repurposing aims to find new therapeutic applications for existing drugs in the pharmaceutical market, leading to significant savings in time and cost. The use of artificial intelligence and knowledge graphs to propose repurposing candidates facilitates the process, as large amounts of data can be processed. However, it is important to pay attention to the explainability needed to validate the predictions. We propose a general architecture to understand several explainable methods for graph completion based on knowledge graphs and design our own architecture for drug repurposing. We present XG4Repo (eXplainable Graphs for Repurposing), a framework that takes advantage of the connectivity of any biomedical knowledge graph to link compounds to the diseases they can treat. Our method allows methapaths of different types and lengths, which are automatically generated and optimised based on data. XG4Repo focuses on providing meaningful explanations to the predictions, which are based on paths from compounds to diseases. These paths include nodes such as genes, pathways, side effects, or anatomies, so they provide information about the targets and other characteristics of the biomedical mechanism that link compounds and diseases. Paths make predictions interpretable for experts who can validate them and use them in further research on drug repurposing. We also describe three use cases where we analyse new uses for Epirubicin, Paclitaxel, and Predinisone and present the paths that support the predictions.
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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.