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

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 towards 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, heterogenous 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 7 drug-drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multimodal 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. © The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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