Several 3D-QSAR models were built based on 196 hepatitis C virus (HCV) NS5A protein inhibitors. The bioactivity values EC₉₀ for three types of inhibitors, the wild type (GT1a) and two mutants (GT1a Y93H and GT1a L31V), were collected to build three datasets. The programs OMEGA and ROCS were used for generating conformations and aligning molecules of the dataset, respectively. Each dataset was randomly divided into a training set and a test set three times to reduce the contingency of only one random selection. QSAR models were computed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the datasets GT1a, GT1a Y93H, and GT1a L31V, the best models CoMFA-INDX, CoMSIA-SEHA, and CoMSIA-SEHA showed an r² value of 0.682 ± 0.033, 0.779 ± 0.036, and 0.782 ± 0.022 on the test sets, respectively. From the contour maps of the three best models, we summarized the favourable and unfavourable substituents on the tetracyclic core, the Z group, the proline group, and the valine group of inhibitors. We guessed the mutants could change the electrostatic surfaces of the wild type active pocket. In addition, we used ECFP analyses to find important substructures and could intuitively understand the results from QSAR models.

基于 196 种丙型肝炎病毒 (HCV) NS5A 蛋白抑制剂构建了几个 3D-QSAR 模型。收集三种类型抑制剂（野生型 (GT1a) 和两种突变体（GT1a Y93H 和 GT1a L31V）的生物活性值 EC ₉₀以构建三个数据集。OMEGA 和 ROCS 程序分别用于生成数据集的构象和对齐分子。每个数据集被随机分为训练集和测试集 3 次，以减少仅一次随机选择的偶然性。通过比较分子场分析 (CoMFA) 和比较分子相似指数分析 (CoMSIA) 计算 QSAR 模型。对于数据集 GT1a、GT1a Y93H 和 GT1a L31V，最佳模型 CoMFA-INDX、CoMSIA-SEHA 和 CoMSIA-SEHA 显示r ²测试集上的值分别为 0.682 ± 0.033、0.779 ± 0.036 和 0.782 ± 0.022。从三个最佳模型的等高线图中，我们总结了抑制剂的四环核心、Z 组、脯氨酸组和缬氨酸组上的有利和不利取代基。我们猜测突变体可以改变野生型活性口袋的静电表面。此外，我们使用 ECFP 分析来寻找重要的子结构，并且可以直观地理解 QSAR 模型的结果。