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The OREGANO knowledge graph for computational drug repurposing.

Marina Boudin1, Gayo Diallo2, Martin Drancé2

  • 1AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France. marina.boudin@u-bordeaux.fr.

Scientific Data
|December 6, 2023
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Summary
This summary is machine-generated.

The OREGANO knowledge graph offers a freely available resource for computational drug repositioning. It integrates diverse drug and natural compound data to accelerate drug discovery through link prediction.

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Area of Science:

  • Computational biology
  • Pharmacology
  • Bioinformatics

Background:

  • Drug repositioning accelerates drug discovery compared to traditional methods.
  • Computational approaches using knowledge graphs show promise for generating drug-target hypotheses.
  • A comprehensive, community-accessible knowledge graph integrating broad drug features is lacking.

Purpose of the Study:

  • To introduce the OREGANO knowledge graph, a novel resource for drug repositioning.
  • To incorporate natural compound data into a comprehensive knowledge graph.
  • To provide open access to the knowledge graph and its associated ETL source codes.

Main Methods:

  • Developed a knowledge graph from scratch by integrating data from multiple sources.
  • Designed a graph model and a node merging strategy for data integration.
  • Implemented an Extract, Transform, Load (ETL) process with data cleaning.

Main Results:

  • Successfully constructed the OREGANO knowledge graph, including natural compound data.
  • The knowledge graph and ETL source codes are publicly available on GitHub.
  • The resource facilitates hypothesis generation for drug repositioning via link prediction.

Conclusions:

  • OREGANO addresses the need for a holistic, community-accessible knowledge graph for drug repositioning.
  • The integrated data and open-source nature of OREGANO can advance computational drug discovery.
  • This resource supports faster and more affordable drug development pipelines.