School of Computer Science and Engineering, Hunan University, Changsha 410012, China.
AWS Shanghai AI Lab, Shanghai 200335, China.
J Proteome Res. 2020 Nov 6;19(11):4624-4636. doi: 10.1021/acs.jproteome.0c00316. Epub 2020 Jul 24.
There have been more than 2.2 million confirmed cases and over 120 000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19.
仅在美国，由新型严重急性呼吸综合征冠状病毒（SARS-CoV-2）引起的人类冠状病毒病 2019（COVID-19）大流行就已确诊超过 220 万例病例，超过 120000 人死亡。然而，目前针对 COVID-19 还没有有效的药物。药物再利用为 COVID-19 的预防和治疗策略的发展提供了一条很有前途的途径。本研究报告了一种综合的、基于网络的深度学习方法，用于鉴定 COVID-19 的可再利用药物（称为 CoV-KGE）。具体来说，我们构建了一个综合的知识图谱，其中包括 39 种关系类型，连接着药物、疾病、蛋白质/基因、途径和从 2400 万篇 PubMed 出版物的大型科学文献集中提取的表达信息，包含 1500 万条边。利用亚马逊的 AWS 计算资源和基于网络的深度学习框架，我们鉴定出了 41 种可再利用药物（包括地塞米松、吲哚美辛、硝氯酚和托瑞米芬），这些药物与 COVID-19 的治疗相关性已通过 SARS-CoV-2 感染的人类细胞中的转录组和蛋白质组学数据以及正在进行的临床试验数据得到验证。虽然这项研究绝不是推荐特定的药物，但它展示了一种强大的深度学习方法，可以优先考虑进一步研究的现有药物，这有可能加速 COVID-19 的治疗开发。
