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.
Keywords: COVID-19; deep representation learning; drug discovery; excessive inflammatory response; heterogeneous drug networks.
【저자키워드】 COVID-19, Drug discovery, deep representation learning, excessive inflammatory response, heterogeneous drug networks., 【초록키워드】 Treatment, coronavirus disease, Necrosis, Tumor, Coronavirus disease 2019, clinical trial, Drug discovery, Inflammatory responses, transcriptomics, interleukin-6, drug, docking, excessive inflammatory response, interleukin, tumor necrosis factor, vector, therapeutic, death, molecular, characteristic, patients, predict, COVID-19 patients, binding, association, Inflammatory response, Inflammatory, tumor necrosis, source code, tumor necrosis factor-α, approaches, complex, heterogeneous, inflammation storm, transcriptomics data, approach, recent, FIVE, significantly, addition, eight, unique, demonstrated, accelerate, downloaded, outperform, 【제목키워드】 anti-inflammatory agent, heterogeneous,