DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19.

Abstract

Recent studies have demonstrated that the excessive inflammatory response is an important factor of death in coronavirus disease 2019 (COVID-19) patients. In this study, we propose a deep representation on heterogeneous drug networks, termed DeepR2cov, to discover potential agents for treating the excessive inflammatory response in COVID-19 patients. This work explores the multi-hub characteristic of a heterogeneous drug network integrating eight unique networks. Inspired by the multi-hub characteristic, we design 3 billion special meta paths to train a deep representation model for learning low-dimensional vectors that integrate long-range structure dependency and complex semantic relation among network nodes. Based on the representation vectors and transcriptomics data, we predict 22 drugs that bind to tumor necrosis factor-α or interleukin-6, whose therapeutic associations with the inflammation storm in COVID-19 patients, and molecular binding model are further validated via data from PubMed publications, ongoing clinical trials and a docking program. In addition, the results on five biomedical applications suggest that DeepR2cov significantly outperforms five existing representation approaches. In summary, DeepR2cov is a powerful network representation approach and holds the potential to accelerate treatment of the inflammatory responses in COVID-19 patients. The source code and data can be downloaded from https://github.com/pengsl-lab/DeepR2cov.git.

• Authors: Xiaoqi Wang, Bin Xin, Weihong Tan, Zhijian Xu, Kenli Li, Fei Li, Wu Zhong, Shaoliang Peng
• Journal: Briefings in bioinformatics (Brief Bioinform) Vol. 22 Issue 6 (11 05 2021) ISSN: 1477-4054 [Electronic] England
• PMID: 34117734 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].

Chemical References

• Anti-Inflammatory Agents

Topics

• Anti-Inflammatory Agents (chemistry, therapeutic use)
• COVID-19 (complications, genetics, virology)
• Computational Biology
• Deep Learning
• Drug Repositioning
• Humans
• Inflammation (complications, drug therapy, genetics, virology)
• Neural Networks, Computer
• SARS-CoV-2 (drug effects, pathogenicity)
• Software
• Transcriptome (drug effects, genetics)
• COVID-19 Drug Treatment