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

Xiangxiang Zeng, Siyi Zhu, Xiangrong Liu, Yadi Zhou, Ruth Nussinov, Feixiong Cheng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

336 Scopus citations

Abstract

Motivation: 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. Results: 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).

Original languageEnglish
Pages (from-to)5191-5198
Number of pages8
JournalBioinformatics
Volume35
Issue number24
DOIs
StatePublished - 15 Dec 2019

Funding

FundersFunder number
National Institutes of HealthR00HL138272
National Heart, Lung, and Blood Institute
National Cancer InstituteZIABC010440
Frederick National Laboratory for Cancer ResearchHHSN261200800001E

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