A Drug Repositioning Approach Using Drug and Disease Features

Abstract

Drug repositioning is an important method in drug discovery. Experiment-based drug discovery is time-consuming and expensive. In recent years, methods based on heterogeneous networks have attracted research interest in this area due to the advantages in this task. By adding features fused from different drug networks and disease features mined from biomedical texts, the prediction effect can be improved. This paper proposes a drug repositioning method using the multi-modal deep autoencoder (MDA) method, which obtains better drug features after fusing several drug networks. Then, in order to predict the links between drug and diseases, disease traits are taken from the text data of biomedical information and combined with the known drug-disease combinations. Specifically, after feature fusion using MDA method, we also use a sparse multi-layer autoencoder (SMAE) to obtain low-dimensional and high-quality drug vector representation, and prove the effectiveness of SMAE module in our ablation experiment. Experimental results indicate that this model can outperform existing methods.