Coronavirus Disease 2019 (COVID-19) has been creating a worldwide pandemic situation. Repurposing drugs, already shown to be free of harmful side effects, for the treatment of COVID-19 patients is an important option in launching novel therapeutic strategies. Therefore, reliable molecule interaction data are a crucial basis, where drug-/protein-protein interaction networks establish invaluable, year-long carefully curated data resources. However, these resources have not yet been systematically exploited using high-performance artificial intelligence approaches. Here, we combine three networks, two of which are year-long curated, and one of which, on SARS-CoV-2-human host-virus protein interactions, was published only most recently (30th of April 2020), raising a novel network that puts drugs, human and virus proteins into mutual context. We apply Variational Graph AutoEncoders (VGAEs), representing most advanced deep learning based methodology for the analysis of data that are subject to network constraints. Reliable simulations confirm that we operate at utmost accuracy in terms of predicting missing links. We then predict hitherto unknown links between drugs and human proteins against which virus proteins preferably bind. The corresponding therapeutic agents present splendid starting points for exploring novel host-directed therapy (HDT) options.
SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19
新型人类冠状病毒COVID-19/SARS-CoV-2的新颖性以及缺乏有效的药物和疫苗，导致采用各种各样的策略来对抗这一全球大流行病。这些策略中的许多依赖于现有药物的重新定位，与从头药物发现相比，这可以缩短时间并降低成本。在这项研究中，我们提出了一种新的基于网络的药物重新定位算法，称为SAveRUNNER（Searching off-lAbel dRUg aNd NEtwoRk），它通过一种新的基于网络的相似性度量来量化药物靶标和人类相互作用组中疾病特异性蛋白质之间的相互作用，从而预测药物-疾病关联，该相似性度量优先考虑位于相同网络邻域中的药物和疾病之间的关联。具体而言，我们将SAveRUNNER应用于14种选定疾病的小组，这些疾病具有关于其致病基因的综合知识，并且已发现与COVID-19相关的遗传相似性，共病或与暂时重新用于治疗COVID-19的药物的关联。特别关注SARS子网络，我们确定了282种可重复使用的药物，包括一些最有传言的COVID-19治疗标签外药物，以及实际用于临床实践的5种药物的新组合疗法。此外，为了最大限度地提高推定的下游验证实验的效率，我们根据其基于网络的相似性值优先考虑了24种潜在的抗SARS-CoV可再利用药物。这些排名靠前的药物包括ACE抑制剂、单克隆抗体和凝血酶抑制剂。最后，我们的研究结果通过基因集富集分析进行了计算机验证，证实了大多数网络预测的可重…
Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
Northeastern University（东北大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Scipher Medicine；Sabanci University（萨班哲大学）；Boston University（波士顿大学）；Central European University（中欧大学）
An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication
Milner Therapeutics Institute, University of Cambridge（剑桥大学米尔纳治疗学研究所）；LifeArc；KAIST（韩国科学技术院）；University College Dublin（都柏林大学学院）；Dataiku；University of London（伦敦大学）；University of Surrey（萨里大学）；Imperial College London（伦敦帝国学院）；Thalesians Ltd（Thalesians有限公司）；The University of Manchester（曼彻斯特大学）
