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.
【저자키워드】 SARS-CoV-2, Coronavirus disease 2019, Protein−protein interaction, Clustering method, 【초록키워드】 COVID-19, Treatment, public health, therapy, Drug discovery, clinical trials, risk, Toxicity, drug, virus, COVID-19 treatments, Protein, Features, death, Cluster, target, respiratory, information, Interaction, confirmed case, biological processes, help, profiles, candidate, candidate drugs, clinical evidence, COVID-19 pathology, while, FIVE, feature, statistical, evaluate, caused, recognize, reduce, build, creating, 【제목키워드】 COVID-19, feature,