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

Topic: Genetics and APOE

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). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR. Supplementary information Supplementary data are available online at Bioinformatics.

Key findings

• 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.
• 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.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1093/bioinformatics/btz418
• PubMed: 31116390
• Original source →

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@article{zeng2019-31116390,
  author  = {Zeng, X. and Zhu, S. and Liu, X. and Zhou, Y. and Nussinov, R. and Cheng, F.},
  title   = {{deepDR: a network-based deep learning approach to in silico drug repositioning}},
  journal = {Bioinformatics (Oxford, England)},
  year    = {2019},
  doi     = {10.1093/bioinformatics/btz418},
  pmid    = {31116390},
  url     = {https://doi.org/10.1093/bioinformatics/btz418}
}

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