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

By Xiangxiang Zeng, Siyi Zhu, Xiangrong Liu, Yadi Zhou, Ruth Nussinov, Feixiong Cheng. Bioinform., 2019

460 citations recorded by the source corpus.

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

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/4192b01075267cd3a461623a88c0bc2e8cd58e89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

In Pharmacology

Jianzhu Ma

Citations in the corpus

Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “The results concluded that deepDR predicted approved drugs such as risperidone and aripiprazole for the treatment of Alzheimer’s disease (AD), whereas methylphenidate and pergolide for treatment of Parkinson’s disease (PD) [291].”
Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells (2020, background): “…of electronic medical records (Esteva et al., 2019; Rajkomar et al., 2019; Wainberg et al., 2018), machine learning models are also of high interest in predicting drug responses (Barretina et al., 2012; Costello et al., 2014; Garnett et al., 2012; Iorio et al., 2016; Zeng et al., 2019).”
Machine learning applications in drug development (2019, methodology): “Automatic feature selection algorithms can take advantage of the great ﬂexibility and learning power of Deep Neural Networks, that, provided the raw input data and the expected outcome, create the most discriminant features [116,117].”
Integration strategies of multi-omics data for machine learning analysis (2021, methodology): “DeepDR [115] for example combined the different features obtained for each graph with a multi-modal autoencoder.”