{"data":{"id":"10.5061/dryad.573n5tbpx","type":"dois","attributes":{"doi":"10.5061/dryad.573n5tbpx","prefix":"10.5061","suffix":"dryad.573n5tbpx","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Papadimas, Fotios","nameType":"Personal","givenName":"Fotios","familyName":"Papadimas","affiliation":["University of Peloponnese"],"nameIdentifiers":[]},{"name":"Svolou, Stavroula","nameType":"Personal","givenName":"Stavroula","familyName":"Svolou","affiliation":["National Centre of Scientific Research \"Demokritos\""],"nameIdentifiers":[]},{"name":"Bougiatiotis, Konstantinos","nameType":"Personal","givenName":"Konstantinos","familyName":"Bougiatiotis","affiliation":["National Centre of Scientific Research \"Demokritos\""],"nameIdentifiers":[]},{"name":"Aisopos, Fotis","nameType":"Personal","givenName":"Fotis","familyName":"Aisopos","affiliation":["National Centre of Scientific Research \"Demokritos\""],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3942-0673","nameIdentifierScheme":"ORCID"}]},{"name":"Krithara, Anastasia","nameType":"Personal","givenName":"Anastasia","familyName":"Krithara","affiliation":["National Centre of Scientific Research \"Demokritos\""],"nameIdentifiers":[]},{"name":"Paliouras, Georgios","nameType":"Personal","givenName":"Georgios","familyName":"Paliouras","affiliation":["National Centre of Scientific Research \"Demokritos\""],"nameIdentifiers":[]}],"titles":[{"title":"Full list of potential drug repurposing candidates for rare neuro-muscular disorders"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"Literature Knowledge Graph"},{"subject":"Graph Neural Networks"},{"subject":"Neuromuscular Disorders"},{"subject":"Link Prediction"},{"subject":"FOS: Medical engineering","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Medical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Basic medicine","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Basic medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-03-23T18:08:42Z","dateType":"Created"},{"date":"2026-03-23T18:08:42Z","dateType":"Submitted"},{"date":"2026-05-05T00:00:00Z","dateType":"Issued"},{"date":"2026-05-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/jamiaopen/ooag050","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://github.com/fotais/simpathic-computational-drug-repurposing","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["618102 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Drug repurposing is particularly challenging yet essential for rare\n diseases, where limited patient populations and scarce biomedical evidence\n hinder traditional discovery pipelines. This work presents a holistic\n machine learning approach for drug-disease link prediction, leveraging\n multiple heterogeneous sources including biomedical literature, structured\n databases, and textual descriptions of diseases. Focusing on seven rare\n neuro-muscular disorders, we construct a biomedical knowledge graph from\n literature and open databases, to evaluate a suite of rule-based, graph\n neural network, and path-encoding models. An ensemble of the\n best-performing methods, further enriched with disease similarity features\n derived from text-based embeddings, is used to generate candidate\n treatments for each disorder. Experimental results show that established\n graph neural network approaches (CompGCN), and path encoding methods\n (Prime Adjacency Matrix framework), outperform other approaches in metrics\n like Mean Reciprocal Rank. The ensemble of the best-performing methods\n further improves those metrics, reaching MRR = 0.3145. A manual validation\n of top-ranked drugs from rare disease experts illustrates a high precision\n (\u0026gt; 50 %) for drugs that potentially treat a rare disorder or its\n symptoms. The lack of vast number of publications and known drug\n indications for rare neuro-muscular disorders sets serious challenges in\n identifying potential therapies and symptom-relievers. The ensemble\n predictor incorporates rule-based, graph neural networks and path encoding\n techniques, to improve drug repurposing prediction performance on a\n biomedical knowledge graph created from open data. Expert evaluation\n indicates that an ensemble of various knowledge graph link prediction\n methods can produce promising repurposing hypotheses, for disorders\n lacking any approved therapies.","descriptionType":"Abstract"},{"description":"# Data from: Identifying Drug Repurposing Candidates for Rare\n Neuro-muscular Disorders, Using Different AI Methods on the Literature\n Knowledge Graph This repository contains the data sources accompanying the\n work: ``` Identifying Drug Repurposing Candidates for Rare Neuro-muscular\n Disorders Using Different AI Methods on the Literature Knowledge Graph\n (2025) Papadimas, F., Svolou, S., Bougiatiotis, K., Aisopos, F., Krithara,\n A., and Paliouras, G. ``` This work was conducted in the context of the\n **SIMPATHIC** project, funded by the European Union’s Horizon 2020\n research and innovation programme (Grant Agreement **No. 101080249**).\n **Drug repurposing** is a critical yet challenging task for rare diseases,\n where limited patient populations and sparse curated biomedical evidence\n hinder traditional drug discovery pipelines. This project presents a\n computational framework for drug–disease link prediction, integrating\n heterogeneous biomedical evidence into a unified literature-based\n knowledge graph. Focusing on seven rare neurological, neurometabolic, and\n neuromuscular disorders: *1. SpinoCerebellar Ataxia type 3 (SCA3)* *2.\n Congenital NeuroTransmitter defects (CNT)* *3. Pyridoxine Dependent\n Epilepsy (PDE )* *4. Congenital disorder glycosylation (PMM2)* *5.\n Zellweger Spectrum Disorders (ZSD)* *6. Myotonic Dystrophy type 1 (DM1)*\n *7. Congenital Myasthenic Syndrome (CMS)* we construct a\n **disease-centered biomedical knowledge graph** and evaluate multiple\n artificial intelligence approaches. ## Description of the data and file\n structure The following two files have been shared, which are used by the\n link prediction approaches: * Drug-Repurposing-Compound-Full-List.tsv: A\n long list of all approved/non-approved drugs and compounds that can be\n considered as candidates for the aforementioned disorders. Various\n information is provided for each drug, namely the Drug Name, Synonyms, CAS\n Number, Drug Target, Drug Pathway(s), Research Area and related Clinical\n Information. Drugs with no synonyms, have \"n/a\" as value in the\n synonyms column. * Known-drug-indications-for-NMDs.tsv: A short list of\n drug indications reported for the aforementioned disorders that is used as\n groundtruth for our prediction methods. This list includes disorder/drug\n names and UMLS CUI identifiers. ## Sharing/Access information These\n indications are collected from the following online databases: * TTD (Zhou\n Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, et al. TTD: Therapeutic Target\n Database describing target druggability information. Nucleic acids\n research. 2024;52(D1):D1465-77. * DrugCentral (Avram S, Wilson TB, Curpan\n R, Halip L, Borota A, Bora A, et al. DrugCentral 2023 extends human\n clinical data and integrates veterinary drugs. Nucleic acids research.\n 2023;51(D1):D1276-87.) * Open Targets (Buniello A, Suveges D,\n Cruz-Castillo C, Llinares MB, Cornu H, Lopez I, et al. Open Targets\n Platform: facilitating therapeutic hypotheses building in drug discovery.\n Nucleic acids research. 2025;53(D1):D1467-75.) * Drugbank (Knox C, Wilson\n M, Klinger CM, Franklin M, Oler E, Wilson A, et al. DrugBank 6.0: the\n DrugBank knowledgebase for 2024. Nucleic acids research.\n 2024;52(D1):D1265-75) ## Code/Software Python 3 was used to implement all\n models presented in this work, except for the Path Analysis feature\n extraction, which relied on a Java-based implementation. GNN-based models,\n R-GCN and CompGCN, were implemented using the Pytorch Geometric library,\n with CompGCN additionally employing PyTorch. The code of all methods is\n provided in the following link:\n [https://github.com/fotais/simpathic-computational-drug-repurposing](https://github.com/fotais/simpathic-computational-drug-repurposing)","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"European Research Council","awardNumber":"101080249","funderIdentifier":"https://ror.org/0472cxd90","funderIdentifierType":"ROR"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4/metadata.xsd">
  <identifier identifierType="DOI">10.5061/DRYAD.573N5TBPX</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Papadimas, Fotios</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/04d4d3c02" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">University of Peloponnese</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Svolou, Stavroula</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/038jp4m40" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">National Centre of Scientific Research "Demokritos"</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Bougiatiotis, Konstantinos</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/038jp4m40" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">National Centre of Scientific Research "Demokritos"</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Aisopos, Fotis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3942-0673</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/038jp4m40" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">National Centre of Scientific Research "Demokritos"</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Krithara, Anastasia</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/038jp4m40" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">National Centre of Scientific Research "Demokritos"</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Paliouras, Georgios</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/038jp4m40" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">National Centre of Scientific Research "Demokritos"</affiliation>
    </creator>
  </creators>
  <titles>
    <title>
      Full list of potential drug repurposing candidates for rare neuro-muscular
      disorders
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2026</publicationYear>
  <subjects>
    <subject>Literature Knowledge Graph</subject>
    <subject>Graph Neural Networks</subject>
    <subject>Neuromuscular Disorders</subject>
    <subject>Link Prediction</subject>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Medical engineering</subject>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Basic medicine</subject>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Computer and information sciences</subject>
  </subjects>
  <fundingReferences>
    <fundingReference>
      <funderName>European Research Council</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/0472cxd90</funderIdentifier>
      <awardNumber>101080249</awardNumber>
      <awardTitle/>
    </fundingReference>
  </fundingReferences>
  <dates>
    <date dateType="Created">2026-03-23T18:08:42Z</date>
    <date dateType="Submitted">2026-03-23T18:08:42Z</date>
    <date dateType="Issued">2026-05-05T00:00:00Z</date>
    <date dateType="Available">2026-05-05T00:00:00Z</date>
  </dates>
  <language>en</language>
  <relatedIdentifiers>
    <relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">https://doi.org/10.1093/jamiaopen/ooag050</relatedIdentifier>
    <relatedIdentifier relationType="IsDerivedFrom" relatedIdentifierType="URL">https://github.com/fotais/simpathic-computational-drug-repurposing</relatedIdentifier>
  </relatedIdentifiers>
  <sizes>
    <size>618102 bytes</size>
  </sizes>
  <version>4</version>
  <rightsList>
    <rights rightsURI="https://spdx.org/licenses/CC0-1.0.html">Creative Commons Zero v1.0 Universal</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">
      Drug repurposing is particularly challenging yet essential for rare
      diseases, where limited patient populations and scarce biomedical evidence
      hinder traditional discovery pipelines. This work presents a holistic
      machine learning approach for drug-disease link prediction, leveraging
      multiple heterogeneous sources including biomedical literature, structured
      databases, and textual descriptions of diseases. Focusing on seven rare
      neuro-muscular disorders, we construct a biomedical knowledge graph from
      literature and open databases, to evaluate a suite of rule-based, graph
      neural network, and path-encoding models. An ensemble of the
      best-performing methods, further enriched with disease similarity features
      derived from text-based embeddings, is used to generate candidate
      treatments for each disorder. Experimental results show that established
      graph neural network approaches (CompGCN), and path encoding methods
      (Prime Adjacency Matrix framework), outperform other approaches in metrics
      like Mean Reciprocal Rank. The ensemble of the best-performing methods
      further improves those metrics, reaching MRR = 0.3145. A manual validation
      of top-ranked drugs from rare disease experts illustrates a high precision
      (&amp;gt; 50 %) for drugs that potentially treat a rare disorder or its
      symptoms. The lack of vast number of publications and known drug
      indications for rare neuro-muscular disorders sets serious challenges in
      identifying potential therapies and symptom-relievers. The ensemble
      predictor incorporates rule-based, graph neural networks and path encoding
      techniques, to improve drug repurposing prediction performance on a
      biomedical knowledge graph created from open data. Expert evaluation
      indicates that an ensemble of various knowledge graph link prediction
      methods can produce promising repurposing hypotheses, for disorders
      lacking any approved therapies.
    </description>
    <description descriptionType="TechnicalInfo">
      # Data from: Identifying Drug Repurposing Candidates for Rare
      Neuro-muscular Disorders, Using Different AI Methods on the Literature
      Knowledge Graph This repository contains the data sources accompanying the
      work: ``` Identifying Drug Repurposing Candidates for Rare Neuro-muscular
      Disorders Using Different AI Methods on the Literature Knowledge Graph
      (2025) Papadimas, F., Svolou, S., Bougiatiotis, K., Aisopos, F., Krithara,
      A., and Paliouras, G. ``` This work was conducted in the context of the
      **SIMPATHIC** project, funded by the European Union’s Horizon 2020
      research and innovation programme (Grant Agreement **No. 101080249**).
      **Drug repurposing** is a critical yet challenging task for rare diseases,
      where limited patient populations and sparse curated biomedical evidence
      hinder traditional drug discovery pipelines. This project presents a
      computational framework for drug–disease link prediction, integrating
      heterogeneous biomedical evidence into a unified literature-based
      knowledge graph. Focusing on seven rare neurological, neurometabolic, and
      neuromuscular disorders: *1. SpinoCerebellar Ataxia type 3 (SCA3)* *2.
      Congenital NeuroTransmitter defects (CNT)* *3. Pyridoxine Dependent
      Epilepsy (PDE )* *4. Congenital disorder glycosylation (PMM2)* *5.
      Zellweger Spectrum Disorders (ZSD)* *6. Myotonic Dystrophy type 1 (DM1)*
      *7. Congenital Myasthenic Syndrome (CMS)* we construct a
      **disease-centered biomedical knowledge graph** and evaluate multiple
      artificial intelligence approaches. ## Description of the data and file
      structure The following two files have been shared, which are used by the
      link prediction approaches: * Drug-Repurposing-Compound-Full-List.tsv: A
      long list of all approved/non-approved drugs and compounds that can be
      considered as candidates for the aforementioned disorders. Various
      information is provided for each drug, namely the Drug Name, Synonyms, CAS
      Number, Drug Target, Drug Pathway(s), Research Area and related Clinical
      Information. Drugs with no synonyms, have "n/a" as value in the
      synonyms column. * Known-drug-indications-for-NMDs.tsv: A short list of
      drug indications reported for the aforementioned disorders that is used as
      groundtruth for our prediction methods. This list includes disorder/drug
      names and UMLS CUI identifiers. ## Sharing/Access information These
      indications are collected from the following online databases: * TTD (Zhou
      Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, et al. TTD: Therapeutic Target
      Database describing target druggability information. Nucleic acids
      research. 2024;52(D1):D1465-77. * DrugCentral (Avram S, Wilson TB, Curpan
      R, Halip L, Borota A, Bora A, et al. DrugCentral 2023 extends human
      clinical data and integrates veterinary drugs. Nucleic acids research.
      2023;51(D1):D1276-87.) * Open Targets (Buniello A, Suveges D,
      Cruz-Castillo C, Llinares MB, Cornu H, Lopez I, et al. Open Targets
      Platform: facilitating therapeutic hypotheses building in drug discovery.
      Nucleic acids research. 2025;53(D1):D1467-75.) * Drugbank (Knox C, Wilson
      M, Klinger CM, Franklin M, Oler E, Wilson A, et al. DrugBank 6.0: the
      DrugBank knowledgebase for 2024. Nucleic acids research.
      2024;52(D1):D1265-75) ## Code/Software Python 3 was used to implement all
      models presented in this work, except for the Path Analysis feature
      extraction, which relied on a Java-based implementation. GNN-based models,
      R-GCN and CompGCN, were implemented using the Pytorch Geometric library,
      with CompGCN additionally employing PyTorch. The code of all methods is
      provided in the following link:
      [https://github.com/fotais/simpathic-computational-drug-repurposing](https://github.com/fotais/simpathic-computational-drug-repurposing)
    </description>
  </descriptions>
</resource>","url":"https://datadryad.org/dataset/doi:10.5061/dryad.573n5tbpx","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":26,"viewsOverTime":[{"yearMonth":"2026-04","total":0},{"yearMonth":"2026-05","total":10},{"yearMonth":"2026-07","total":2},{"yearMonth":"2026-08","total":4},{"yearMonth":"2026-09","total":10}],"downloadCount":11,"downloadsOverTime":[{"yearMonth":"2026-04","total":1},{"yearMonth":"2026-05","total":5},{"yearMonth":"2026-07","total":1},{"yearMonth":"2026-08","total":0},{"yearMonth":"2026-09","total":4}],"referenceCount":0,"citationCount":1,"citationsOverTime":[{"year":"2026","total":1}],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-05T20:45:26.000Z","registered":"2026-05-05T20:45:27.000Z","published":"2026","updated":"2026-05-05T20:45:27.000Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}},"provider":{"data":{"id":"dryad","type":"providers"}},"media":{"data":{"id":"10.5061/dryad.573n5tbpx","type":"media"}},"references":{"data":[]},"citations":{"data":[{"id":"10.1093/jamiaopen/ooag050","type":"dois"}]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}