Department of Chemistry, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran.
Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
SAR QSAR Environ Res. 2021 Jun;32(6):495-520. doi: 10.1080/1062936X.2021.1925344.
Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients () of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC values were predicted based on the average prediction of ten models.
建立了稳健可靠的定量构效关系模型，用于通过蒙特卡罗技术预测丙型肝炎病毒 NS3/4A 蛋白酶抑制剂的半数最大抑制浓度（IC）值。从科学文献中提取了 524 种 HCV NS3/4A 蛋白酶抑制剂，以创建一个相当大的数据集。该模型是使用 CORAL 软件通过使用两个目标函数开发的，即不应用相关性理想指数（IIC）的目标函数 1（TF1）和使用 IIC 的目标函数 2（TF2）。基于 TF2 构建的模型在统计学上比基于 TF1 的模型更显著和稳健。基于 TF2 的最佳拆分的训练集和测试集的确定系数（）分别为 0.86 和 0.88。还详细提取和解释了活性增加/减少的促进剂。模型解释结果解释了不同结构属性在预测丙型肝炎病毒 NS3/4A 蛋白酶抑制剂的 pIC 值方面的作用。基于机制模型解释结果，设计了 8 种新化合物，并基于 10 种模型的平均预测值预测了它们的 pIC 值。
