Using as a template the crystal structure of the SARS-CoV-2 main protease, we developed a pharmacophore model of functional centers of the protease inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of the SARS-CoV-2 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of the protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin—the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.

我们以 SARS-CoV-2 主要蛋白酶的晶体结构为模板，开发了蛋白酶抑制剂结合口袋功能中心的药效团模型。利用这个模型，我们对 FDA 批准的药物的构象数据库进行了数据挖掘。这项搜索带来了 64 种可能成为 SARS-CoV-2 蛋白酶潜在抑制剂的化合物。这些化合物的构象经历了 3D 指纹相似性聚类。然后我们将这些药物的可能构象异构体与蛋白酶的结合口袋对接。我们还对随机化合物进行了相同的对接。所选化合物的对接相互作用的自由能明显低于随机化合物。三种选定的化合物是卡非佐米、环孢素 A、和阿奇霉素——已经过测试用于 COVID-19 治疗的药物。在选定的化合物中，有两种 HIV 蛋白酶抑制剂和两种丙型肝炎蛋白酶抑制剂。我们建议测试用于治疗 COVID-19 的选定化合物。