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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-\u03b1 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.<\/jats:p>","DOI":"10.1093\/bib\/bbab226","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T15:12:59Z","timestamp":1621869179000},"source":"Crossref","is-referenced-by-count":44,"title":["DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19"],"prefix":"10.1093","volume":"22","author":[{"given":"Xiaoqi","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Xin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, 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