Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and, thus, repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a new restraint energy distribution (RED) function designed to accelerate the FEP-ABFE calculations and make the practical FEP-ABFE-based virtual screening of the existing drug library possible for the first time. As a result, out of twenty-five drugs predicted, fifteen were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (Ki=0.04 uM) which has showed promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki=0.36 uM) and chloroquine (Ki=0.56 uM) were also found to potently inhibit SARS-CoV-2 Mpro for the first time. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

中文翻译：

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通过对现有药物进行基于加速的自由能扰动的虚拟筛选，确定有效的SARS-CoV-2主要蛋白酶抑制剂

由严重急性呼吸系统综合症冠状病毒2（SARS-CoV-2）引起的2019年冠状病毒病（COVID-19）大流行已成为全球性危机。没有针对COVID-19的治疗方法。非常需要从可用于其他疾病的现有药物中鉴定出针对SARS-CoV-2的潜在抗病毒剂，从而将其重新用于治疗COVID-19。通常，用于治疗新疾病（例如COVID-19）的药物调整工作通常始于对现有药物进行虚拟筛选，然后进行实验验证，但是使用传统的计算方法，实际命中率通常较低。在这里，我们报告了一种新的虚拟筛选方法，该方法具有基于加速自由能摄动的绝对结合自由能（FEP-ABFE）预测，及其在鉴定靶向SARS-CoV-2主要蛋白酶（Mpro）的药物中的用途。准确的FEP-ABFE预测是基于使用新的约束能量分布（RED）函数而设计的，该函数旨在加速FEP-ABFE计算并首次使基于FEP-ABFE的实际虚拟筛选现有药物库成为可能。结果，在预测的25种药物中，有15种被确认为SARS-CoV-2 Mpro的有效抑制剂。最有效的是双嘧达莫（Ki = 0.04 uM），其在随后进行的临床研究中对COVID-19患者的治疗已显示出有希望的治疗效果。另外，羟氯喹（Ki ＝ 0.36uM）和氯喹（Ki ＝ 0。还发现56 uM）首次有效抑制SARS-CoV-2 Mpro。我们预计基于FEP-ABFE预测的虚拟筛选方法将在许多其他药物重新利用或发现工作中有用。