Aims: To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications.

Background: SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally.

Objective: To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs.

Methods: A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol.

Results: Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications.

Conclusion: Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.

目的：通过使用分子对接技术对用于其他临床应用的药物进行虚拟筛选，预测COVID-19的潜在药物。

背景：SARS-CoV-2是导致COVID-19大流行的乙型冠状病毒。由于高死亡率，高基本繁殖数量和缺乏临床批准的药物和疫苗，世界卫生组织将其列为潜在的全球健康威胁。负责COVID-19的病毒基因组已测序。另外，已经通过实验确定了主要蛋白酶的三维结构。

目的：通过基于分子对接的所有批准药物的虚拟筛选，鉴定可用于治疗COVID-19的潜在药物。

方法：从SuperDRUG2数据库中获得批准用于临床的药物清单。从RCSB PDB获得了载脂蛋白形式的靶的结构以及几种靶-配体复合物的结构。由X射线衍射数据确定的SARS-CoV-2 Mpro的结构用作靶。有关COVID-19临床试验中药物的数据可从Clinicaltrials.org获得。通过使用Obabel和自定义的python，bash和awk脚本，准备了基于分子对接的虚拟筛选的输入。用Vina和SMINA进行分子对接计算，并用PLIP，Pymol和Rasmol分析和可视化对接的构象。

结果：在针对COVID-19的临床试验中测试的药物中，Danoprevir和Darunavir被预测对SARS-CoV-2的主要蛋白酶（Mpro）靶标具有最高的结合亲和力。通过使用批准用于其他临床适应症的药物的虚拟筛选，沙奎那韦和贝克拉布韦被确定为COVID-19治疗的最佳新候选药物。

结论：被批准用于治疗其他病毒性疾病的蛋白酶抑制剂有可能被重新用于治疗COVID-19。