118
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      scite_
       
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Explainable drug repurposing via path-based knowledge graph completion

      Preprint
      In review
      research-article
      Bookmark

            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 focusses 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.

            Content

            Author and article information

            Journal
            DrugRxiv
            REPO4EU
            10 October 2023
            Affiliations
            [1 ] Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Madrid Spain ( https://ror.org/03n6nwv02)
            Author notes
            Author information
            https://orcid.org/0009-0003-8679-4680
            https://orcid.org/0009-0002-6177-1071
            https://orcid.org/0000-0002-7028-3179
            https://orcid.org/0000-0001-9073-7927
            Article
            10.58647/DRUGARXIV.PR000002.v1
            becec7c2-6f34-4a97-ae9f-b58188f03211

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 10 October 2023
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/100018696, HORIZON EUROPE Health;
            Categories

            The datasets generated during and/or analysed during the current study are available in the repository: https://github.com/hetio/hetionet
            Bioinformatics & Computational biology
            Drug Repurposing,Heterogeneous Knowledge Graphs,Knowledge Graph Completion,Interpretability,Hetionet,Rule-based link prediction

            Comments

            Comment on this article