Drug target ontology to classify and integrate drug discovery data

Research output: Contribution to journalJournal articleResearchpeer-review


  • Yu Lin
  • Saurabh Mehta
  • Hande Küçük-McGinty
  • John Paul Turner
  • Dusica Vidovic
  • Michele Forlin
  • Amar Koleti
  • Dac-Trung Nguyen
  • Rajarshi Guha
  • Stephen L Mathias
  • Oleg Ursu
  • Vasileios Stathias
  • Jianbin Duan
  • Nooshin Nabizadeh
  • Caty Chung
  • Christopher Mader
  • Ubbo Visser
  • Jeremy J Yang
  • Cristian G Bologa
  • Tudor I Oprea
  • Stephan C Schürer

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.

RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.

CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Original languageEnglish
Article number50
JournalJournal of Biomedical Semantics
Issue number1
Number of pages16
Publication statusPublished - 9 Nov 2017

    Research areas

  • Journal Article

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