论文标题
使用图神经网络和协调多个证据的药物重新利用covid-19
Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
论文作者
论文摘要
在2019年新型冠状病毒疾病（Covid-19）感染了SARS-COV-2的大流行中，已经迅速进行了预防和治疗的大量药物研究，但到目前为止，这些努力一直没有成功。我们的目标是使用重新利用的管道确定重新定位药物的优先级，该药物有系统地整合多个SARS-COV-2和药物相互作用，深度图神经网络以及基于维特罗/人群的验证。我们首先通过CTDBase收集了参与COVID-19患者治疗的所有可用药物（n = 3,635）。我们根据病毒诱饵，宿主基因，途径，药物和表型之间的相互作用建立了SARS-COV-2知识图。使用深度图神经网络方法来基于生物学相互作用来得出候选代表。我们使用临床试验病史将候选药物优先考虑，然后通过其遗传特征，体外实验疗效和电子健康记录对其进行了验证。我们重点介绍了包括阿奇霉素，阿托伐他汀，阿司匹林，对乙酰氨基酚和沙丁胺醇在内的前22种药物。我们进一步指出了可能协同靶向共vid-19的药物组合。总而言之，我们证明了广泛相互作用，深层神经网络和严格验证的整合可以促进快速鉴定候选药物进行COVID-19治疗。这是科学报告中发表的文章的Poster-Poper-Review，Copyedit版本，最终身份验证的版本可在线获得：https：//www.nature.com/articles/s41598-021-021-021-02353-5-5
Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This is a post-peer-review, pre-copyedit version of an article published in Scientific Reports The final authenticated version is available online at: https://www.nature.com/articles/s41598-021-02353-5
