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Network Representation Learning-Based Drug Mechanism Discovery and Anti-Inflammatory Response Against COVID-19
preprintrevised on 24.06.2020, 11:10 and posted on 26.06.2020, 11:47 by Wang Xiaoqi, Bin Xin, Zhijian Xu, Kenli LI, Fei Li, Wu Zhong, Weihong Tan, Shaoliang Peng
Recent studies have been demonstrated that host immune imbalance is an important factors leading to acute respiratory distress syndrome (ARDS) in COVID-19 patients. Therefore, discovery of potential drugs and identification of their mechanisms of action for the prevention of immune imbalance in COVID-19 patients are urgently needed. In this study, we proposed a network representation learning-based methodology, termed AIdrug2cov, to discover drug mechanism and anti-inflammatory response for patients with COVID19. In AIdrug2cov, a deep bidirectional Transformer encoder network representation approach is developed to automatically learn lowdimensional vector of heterogeneous network. Using the representation vectors, AIdrug2cov identifies 40 potential targets and 24 high-confidence drugs that bind to tumor necrosis factor(TNF)-α or interleukin(IL)-6 to prevent excessive inflammatory responses in COVID-19 patients. In particular, AIdrug2cov indicated that chloroquine and hydroxychloroquine are able to reduce fatality of COVID-19 patients, and that their mechanisms of action are likely mediated through their inhibition of inflammatory cytokines on top of their antiviral ability, consistent with the findings of clinical studies. In addition, the results in 5 pharmacological application suggested that AIdrug2cov significantly outperforms 5 other state-of-the-art network representation approaches, future demonstrating the availability of AIdrug2cov in drug development field. In summary, AIdrug2cov is practically useful for accelerating COVID-19 therapeutic development. The source code and data can be downloaded from https://github.com/pengsl-lab/AIdrug2cov.git