The use of knowledge graphs for drug repurposing: From classical machine learning algorithms to graph neural networks
Siqi Wei, Christo Sasi, Jelle Piepenbrock, Martijn A. Huynen, Peter A.C. ’t Hoen
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. • Overview of AI methods using knowledge graphs for drug repurposing. • Comparison of mainstream tools for drug repurposing via knowledge graphs. • Suggestions for further improvements of drug repurposing algorithms.