Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data

Adrián Ayuso Muñoz, Lucía Prieto Santamaría, Esther Ugarte Carro, Emilio Serrano, Alejandro Rodríguez González

Abstract: Highlights•A complex set of biomedical information from different sources has been integrated.•A large biomedical graph with 5 node and 12 edge types was constructed.•Graph Neural Networks were used to predict links between drugs and diseases.•Five of the top novel predictions by the model and their plausibility were studied.•Model achieves 0.9604 AUROC and 0.9518 AUPRC for the presented secondary test.