Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases

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.

针对近期暴发的新型冠状病毒肺炎（COVID-19）疫情，本研究以严重急性呼吸综合征冠状病毒3CL蛋白酶（SARS-CoV-3CLpro）抑制剂为研究对象，开展了二维定量构效关系（two-dimensional quantitative structure-activity relationship, 2D-QSAR）建模工作，构建了基于多元线性回归（multiple linear regression, MLR）的预测模型。本研究选用二维描述符，旨在搭建易于解释、可迁移且可重复的预测模型，以实现筛选流程中候选化合物的SARS-CoV-3CLpro抑制活性快速预测。基于所构建的2D-QSAR模型所得分析结果，本研究明确了可提升3CL蛋白酶抑制活性的关键结构特征。此外，本研究选取数据集中活性最高与最低的分子开展分子对接分析，以解析结合过程中的分子相互作用机制，并将所得结果与2D-QSAR模型得到的核心结构特征进行关联验证。另外，本研究依托所构建的模型，对从两个抗病毒药物数据库（即美国化学会（CAS）COVID-19抗病毒候选化合物数据库，以及ZINC15数据库中近期报道的优先化合物列表）获取的共计50437个化合物开展了SARS-CoV-3CLpro抑制活性的虚拟预测，并筛选出优先候选化合物以供后续实验验证其对SARS-CoV-3CLpro的抑制性能。