deepDR: a network-based deep learning approach toin silicodrug repositioning

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). 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.

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

“…This methodology allows to identify several candidate repurposable drugs for Ebola virus 11,14 . Our work over the last decade has demonstrated how network strategies can, for example, be used to identify effective repurposable drugs 13,[22][23][24][25][26][27] and drug combinations 28 for multiple human diseases. For example, network-based drug-disease proximity sheds light on the relationship between drugs (e.g., drug targets) and disease modules (molecular determinants in disease pathobiology modules within the PPIs), and can serve as a useful tool for efficient screening of potentially new indications for approved drugs, as well as drug combinations, as demonstrated in our recent studies 13,23,27,28 .…”

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

“…Feature selection algorithms are widely used in machine learning Zeng et al, 2019a;Zeng et al, 2019b), and FIGURE 2 | Schematic showing the process of extracting features from the transition probability matrix of the DNA sequence. The sequence "AATACATGGGGTTATGTGCCACCGGTCATAATATCTAGGGT" is used as an example to explain the process.…”

Network-based Drug Repurposing for Human Coronavirus

“…This methodology allows to identify several candidate repurposable drugs for Ebola virus [10,13] . Our work over the last decade has demonstrated how network strategies can, for example, be used to identify effective repurposable drugs [12,[21][22][23][24] and drug combinations [25] for multiple human diseases. For example, network-based drug-disease proximity that sheds light on the relationship between drugs (e.g., drug targets) and disease modules (molecular determinants in disease pathobiology modules within the PPIs), and can serve as a useful tool for efficient screening of potentially new indications for approved drugs, as well as drug combinations, as demonstrated in our recent studies [12,22,25] .…”