Background: The quantitative structure-activity relationship (QSAR) approach is most widely used for the prediction of biological activity of potential medicinal compounds. A QSAR model is developed by correlating the information obtained from chemical structures (numerical descriptors/ independent variables) with the experimental response values (the dependent variable).

Methods: In the current study, we have developed a QSAR model to predict the inhibitory activity of small molecule carboxamides against severe acute respiratory syndrome coronavirus (SARS-- CoV) 3CLpro enzyme. Due to the structural similarity of this enzyme with SARS-CoV-2, the causative organism of the recent pandemic, the former may be used for the development of therapies against coronavirus disease 19 (COVID-19).

Results: The final multiple linear regression (MLR) model was based on four two-dimensional descriptors with definite physicochemical meaning. The model was strictly validated using different internal and external quality metrics. The model showed significant statistical quality in terms of determination coefficient (R²=0.748, adjusted R² or R² _adj = 0.700), cross-validated leave-one-out Q² (Q²=0.628) and external predicted variance R² _pred = 0.723. The final validated model was used for the prediction of external set compounds as well as to virtually design a new library of small molecules. We have also performed a docking analysis of the most active and least active compounds present in the dataset for comparative analysis and to explain the features obtained from the 2D-QSAR model.

Conclusion: The derived model may be useful to predict the inhibitory activity of small molecules within the applicability domain of the model only based on the chemical structure information prior to their synthesis and testing.

背景：定量构效关系 (QSAR) 方法最广泛用于预测潜在药用化合物的生物活性。通过将从化学结构（数值描述符/自变量）获得的信息与实验响应值（因变量）相关联来开发 QSAR 模型。

方法：在目前的研究中，我们建立了一个QSAR模型来预测小分子甲酰胺对严重急性呼吸综合征冠状病毒（SARS-CoV）3CLpro酶的抑制活性。由于这种酶与最近大流行的病原体 SARS-CoV-2 的结构相似，前者可用于开发针对冠状病毒病 19 (COVID-19) 的疗法。

结果：最终的多元线性回归（MLR）模型基于四个具有明确物理化学意义的二维描述符。该模型使用不同的内部和外部质量指标进行了严格验证。^{该模型在决定系数（R 2} =0.748，调整后的 R ²或 R ² _adj = 0.700）、交叉验证留一法 Q ²（Q ² =0.628）和外部预测方差 R ² 方面显示出显着的统计质量_预测= 0.723。最终验证的模型用于预测外部设定的化合物以及虚拟设计一个新的小分子库。我们还对数据集中存在的最活跃和最不活跃的化合物进行了对接分析，以进行比较分析并解释从 2D-QSAR 模型获得的特征。

结论：导出的模型可能有助于预测模型适用范围内的小分子的抑制活性，仅基于它们合成和测试之前的化学结构信息。