The use of knowledge graphs for drug repurposing: From classical machine learning algorithms to graph neural networks.

Journal: Computers in biology and medicine
Published Date: Aug 9, 2025

Abstract

Drug repurposing, the development of new therapeutic indications for existing drugs, is a promising strategy in drug development. Computational methods and artificial intelligence may be used to identify new drug repurposing candidates. Knowledge graph (KG) based methods have emerged as powerful tools for modeling and predicting drug-disease relationships, because of their intuitive way of exploiting biomedical knowledge and data. This review provides an overview of computational drug repurposing methods based on KGs. The motivation for adopting KG-based knowledge representations, traditional machine learning and deep learning approaches are discussed, followed by an analysis of selected tools, their construction, link prediction capabilities, and inherent advantages and limitations.

Authors

  • Siqi Wei
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China.
  • Christo Sasi
    Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands; Faculty of Science, Radboud University, Nijmegen, 6525 AJ, The Netherlands.
  • Jelle Piepenbrock
    Faculty of Science, Radboud University, Nijmegen, 6525 AJ, The Netherlands.
  • Martijn A Huynen
    Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands.
  • Peter A C 't Hoen
    Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

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