KGCN-DDA: A Knowledge Graph Based GCN Method for Drug-Disease Association Prediction

Exploring the potential efficacy of a drug is a valid approach for drug discovery with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. A drug repositioning knowledge graph of drugs, diseases, targets, genes and side effects was introduced in our study to impose an explicit structure to integrate heterogeneous biomedical data. We revealed drug and disease embeddings from the constructed knowledge graph via a two-layer graph convolutional network with an attention mechanism. Finally, KGCN-DDA achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916, a relative improvement of 31.67% and 16.09%, respectively, over the second-best results of the four existing state-of-the-art prediction methods. Meanwhile, case studies have verified that KGCN-DDA can discover new associations to accelerate drug discovery.