An explainable framework for drug repositioning from disease information network

Abstract

Exploring efficient and high-accuracy computational drug repositioning methods has become a popular and attractive topic in drug development. This technology can systematically identify potential drug-disease interactions, which could greatly alleviate the pressures from the high cost and long period taken by traditional drug research and discovery. However, plenty of current computational drug repositioning approaches lack interpretability in predicting drug-disease associations, which will not be friendly to their subsequent in-depth research. To this end, we hereby propose a novel computational framework, called EDEN, for exploring explainable drug repositioning from the disease information network (DIN). EDEN is a graph neural network framework that learns the local semantics and global structure of the DIN, and models the drug-disease associations into the DIN by maximizing the mutual information of both and an end-to-end manner. In this way, the learned biomedical entity and link embeddings are enabled to retain the ability to drug repositioning with the semantical structure of external knowledge, thereby making interpretation possible. Meanwhile, we also propose a matching score based on the final embeddings to generate the predictive drug repositioning explanation. Empirical results on the real-world dataset show that EDEN outperforms other state-of-the-art baselines on most of the metrics. Further studies reveal the effectiveness of the explainability of our approach.

Topics & Concepts