Drug repurposing with graph neural networks Towards treatment for COVID-19

dc.contributor.advisor't Hoen, Peter-Bram
dc.contributor.advisorHeskes, Tom
dc.contributor.authorWillems, Lotte
dc.description.abstractDrug repurposing has great promises for the field of drug development as it reduces the cost and duration of the development process as well as reducing potential risks of this process. The recently released Drug Repurposing Knowledge Graph (DRKG) combines information from existing databases, making it a generic biomedical knowledge graph for drug repurposing [1]. Recent advances in artificial intelligence (AI) have made it easier than ever to analyse these big biomedical knowledge graphs. This thesis investigated whether a relational graph autoencoder (R-GAE) can be used to derive potential drug repurposing candidates for COVID-19 from a biomedical knowledge graph. During the thesis, it was investigated whether enhancing a generic knowledge graph with disease-specific data results in a better and more relevant prediction of potential drug repurposing candidates. The results suggest that adding disease-specific data to a generic knowledge graph results in different predicted drug repurposing candidates compared to a generic knowledge graph. Even though there are pitfalls in the approach used in this study, the attempts that have been made offer promising insights in the use of graph neural networks for drug repurposing. Future research will have to investigate how to overcome the limitations towards better predictions on disease-specific drug repurposing candidates.
dc.thesis.facultyFaculteit der Sociale Wetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Master Artificial Intelligence
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence
dc.titleDrug repurposing with graph neural networks Towards treatment for COVID-19
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