deepDR: a network-based deep learning approach to in silico drug repositioning

• Published in: Bioinformatics (Oxford, England) Vol. 35; no. 24; pp. 5191 - 5198
• Main Authors: Zeng, Xiangxiang, Zhu, Siyi, Liu, Xiangrong, Zhou, Yadi, Nussinov, Ruth, Cheng, Feixiong
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.12.2019
• Subjects: Computational Biology; Computer Simulation; Deep Learning; Drug Repositioning; Original Papers; Software
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btz418

Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug-disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug-disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer's disease (e.g. risperidone and aripiprazole) and Parkinson's disease (e.g. methylphenidate and pergolide). Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR. Supplementary data are available online at Bioinformatics.