Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug–target and protein−protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.

中文翻译：

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在基于机器学习的方法中使用信息特征进行 COVID-19 药物再利用

2019 年冠状病毒病 (COVID-19) 是由一种名为严重急性呼吸系统综合症冠状病毒-2 (SARS-CoV-2) 的新型病毒引起的。该病毒在全球造成大量死亡和数百万确诊病例，对公共健康造成严重威胁。然而，目前尚无可用于治疗 COVID-19 的特定疗法或药物。虽然新药发现是一个漫长的过程，但重新利用现有的 COVID-19 药物可以帮助识别具有已知临床特征的治疗方法。计算药物再利用方法可以降低成本、时间和药物毒性风险。在这项工作中，我们构建了一个图作为与 COVID-19 相关的生物网络。该网络与病毒目标或其相关的生物过程有关。我们在构建的生物网络中选择导致网络重大破坏的必需蛋白质。我们的方法从这些必需蛋白质中选择了 93 种与 COVID-19 病理学相关的蛋白质。然后，我们根据药物-靶点和蛋白质-蛋白质相互作用信息提出多种信息特征。通过这些信息丰富的特征，我们找到了五种合适的药物簇，其中包含一些作为潜在的 COVID-19 治疗药物的候选药物。为了评估我们的结果，我们为候选药物提供统计和临床证据。从我们提出的候选药物中，80%已经在其他研究和临床试验中进行了研究。