Drug-disease association prediction based on end-to-end multi-layer heterogeneous graph convolutional encoders

Abstract

Traditional drug development in wet labs has long been a costly, cumbersome and error-prone process. Thus, taking advantage of computational power to create algorithmic methods for identifying new treatments for approved drugs is of high importance. Most of the previous works have been strongly relying on the drug's features as well as the similarities between the biological entities to predict potential associations between drugs and diseases. However, the relationships between different biological entities provide crucial information and, unlike previously conducted researches, can be used as the knowledge graph for drug-disease pair interaction prediction tasks. In this study, a novel end-to-end multi-layers heterogeneous graph convolutional encoder (LHGCE) has been proposed to predict candidate associations between drugs and diseases. Firstly, it creates multiple networks between different biological entities, then combines them to build a heterogeneous network as a knowledge graph to capture the topological information of the nodes in the network context. After constructing the network and applying a message-passing operation, the embeddings of the drugs and diseases of each drug-disease pair is extracted and fed into the linear layers to predict the potential interactions. The experimental results based on 5-fold cross-validation showed that LHGCE outperforms state-of-the-art methods in terms of the most evaluation metrics.