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

DeepR2cov is a deep representation on heterogeneous drug networks 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 three 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(TNF)-α or interleukin(IL)-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.  DeepR2cov is a powerful network representation approach, and holds the potential to accelerate treatment of the inflammatory responses in COVID-19 patients.