ArticlePloS one2014
Graph theory enables drug repurposing--how a mathematical model can drive the discovery of hidden mechanisms of action.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The algebraic extended atom-type graph-based model for precise ligand-receptor binding affinity prediction.Journal of cheminformatics · 2025Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- Computationally repurposing drugs for breast cancer subtypes using a network-based approach.BMC bioinformatics · 2022Article
- Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing.Medical sciences (Basel, Switzerland) · 2022Review
- Computational repurposing of therapeutic small molecules from cancer to pulmonary hypertension.Science advances · 2021Article
- Topological network measures for drug repositioning.Briefings in bioinformatics · 2021Article
- An Open Access Database of Licensed Cancer Drugs.Frontiers in pharmacology · 2021Article
- Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work.European journal of medicinal chemistry · 2019Review
- The use or generation of biomedical data and existing medicines to discover and establish new treatments for patients with rare diseases - recommendations of the IRDiRC Data Mining and Repurposing Task Force.Orphanet journal of rare diseases · 2019Article
- Review of Drug Repositioning Approaches and Resources.International journal of biological sciences · 2018Review
- Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.Gastroenterology · 2017Review
- A graph-based approach to construct target-focused libraries for virtual screening.Journal of cheminformatics · 2016Article
- Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma.Studies in health technology and informatics · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.
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