新型冠状病毒(SARS-CoV-2) RNA聚合酶抑制剂的密度泛函理论计算辅助筛选

Density Functional Theory Calculation Aided Screening of SARS-CoV-2 RNA Polymerase Inhibitors

摘要: 瑞德西韦(Remdesivir, GS-5734)是一种在研的核苷类RNA聚合酶抑制剂，被认为是治疗新冠肺炎(COVID-19)的候选药物，现已被批准用于临床试验阶段。本文以瑞德西韦为参考，基于密度泛函理论(DFT)，对六种RNA聚合酶抑制剂进行了进一步的量子化学辅助筛选。将六种药物分子的电子结构性质与瑞德西韦进行了系统的对比，发现化合物6的偶极矩、能隙值、概念密度泛函参数均与瑞德西韦相近，因此推测该化合物也可能是一种潜在的SARS-CoV-2病毒RNA聚合酶的抑制剂。最终，通过分子对接实验进一步证实了化合物6对SARS-CoV-2 RNA聚合酶的潜在抑制能力。本文有望为候选药物的进一步筛选提供一种量子化学计算辅助手段。

Abstract: Remdesivir (GS-5734) is a valuable nucleoside RNA polymerase inhibitor, which is considered to be a potential drug for curing COVID-19 and has been used in clinical trials now. In this paper, six potential RNA polymerase inhibitors were compared with Remdesivir based on the density func-tional theory (DFT). By comparing electronic structure properties of the chosen inhibitors with Remdesivir, it is found that the dipole moment, energy gap, and conceptual density functional pa-rameters of compound 6 were similar to those of Remdesivir, indicating its potential of serving as inhibitor of SARS-CoV-2 RNA polymerase. Moreover, the potential inhibitory ability of compound 6 against SARS-CoV-2 was further confirmed by molecular docking study. This paper is expected to provide a quantum chemical computation aided strategy for further screening drug candidates.