ABSTRACT

In the context of recently emerged pandemic of COVID-19, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) modelling using SARS-CoV-3CLpro enzyme inhibitors for the development of a multiple linear regression (MLR) based model. We have used 2D descriptors with an aim to develop an easily interpretable, transferable and reproducible model which may be used for quick prediction of SAR-CoV-3CLpro inhibitory activity for query compounds in the screening process. Based on the insights obtained from the developed 2D-QSAR model, we have identified the structural features responsible for the enhancement of the inhibitory activity against 3CLpro enzyme. Moreover, we have performed the molecular docking analysis using the most and least active molecules from the dataset to understand the molecular interactions involved in binding, and the results were then correlated with the essential structural features obtained from the 2D-QSAR model. Additionally, we have performed in silico predictions of SARS-CoV 3CLpro enzyme inhibitory activity of a total of 50,437 compounds obtained from two anti-viral drug databases (CAS COVID-19 antiviral candidate compound database and another recently reported list of prioritized compounds from the ZINC15 database) using the developed model and provided prioritized compounds for experimental detection of their performance for SARS-CoV 3CLpro enzyme inhibition.

摘要

在最近出现的COVID-19大流行的背景下，我们使用SARS-CoV-3CLpro酶抑制剂进行了二维定量结构-活性关系（2D-QSAR）建模，用于开发基于多元线性回归（MLR）的模型。我们使用了二维描述符，目的是开发一个易于解释，可转移和可再现的模型，该模型可用于快速预测筛选过程中查询化合物的SAR-CoV-3CLpro抑制活性。基于从已开发的2D-QSAR模型获得的见解，我们确定了负责增强对3CLpro酶的抑制活性的结构特征。此外，我们使用数据集中活性最高和最低的分子进行了分子对接分析，以了解参与结合的分子相互作用，然后将结果与从2D-QSAR模型获得的基本结构特征相关联。此外，我们对从两个抗病毒药物数据库（CAS COVID-19抗病毒候选化合物数据库和最近从ZINC15优先报道的化合物列表）中获得的总共50,437种化合物进行了SARS-CoV 3CLpro酶抑制活性的计算机模拟预测数据库），并提供了优先化合物用于实验检测其对SARS-CoV 3CLpro酶的抑制作用。