Vyas Vivek K, Ghate Manjunath, Chintha Chetan, Patel Paresh
Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, S.G. Highway, Charodi, Ahmedabad 382 481 Gujarat, India.
Curr Comput Aided Drug Des. 2013 Sep;9(3):433-45. doi: 10.2174/15734099113099990028.
This study investigated 3D quantitative structure-activity relationships (QSAR) for a range of substituted benzimidazole derivatives as AngII-AT1 receptor antagonists by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA). The alignment strategy was used for these compounds by means of Distill function defined in SYBYL X 1.2. The best CoMFA and CoMSIA models were obtained for the training set compounds was statistically significant with leave-one-out (LOO) validation correlation coefficient (q²) of 0.613 and 0.622, cross validated coefficient (r²cv) of 0.617 and 0.607, respectively and conventional coefficient (r²ncv) of 0.886 and 0.859, respectively. Both the models were validated by a test set of 18 compounds giving satisfactory predicted correlation coefficient (r²pred) of 0.714 and 0.549 for CoMFA and CoMSIA models, respectively. Generated 3D QSAR models were used for the prediction of pIC50 of an external dataset of 10 compounds for predictive validation, which gave conventional r² of 0.893 for CoMFA model, and 0.774 for CoMSIA model. We identified some key features in substituted benzimidazole derivatives, such as the importance of lipophilicity and H-bonding at 2- and 5, 6, 7- position of benzimidazole ring, respectively, for good antagonistic activity. CoMFA and CoMSIA models generated in this work provide useful information for the design of new compounds and helped in prediction of antagonistic activity.
本研究通过比较分子场分析（CoMFA）和比较分子相似性指数（CoMSIA）研究了一系列取代苯并咪唑衍生物作为血管紧张素II - AT1受体拮抗剂的三维定量构效关系（QSAR）。借助SYBYL X 1.2中定义的Distill函数对这些化合物进行比对策略。训练集化合物获得的最佳CoMFA和CoMSIA模型具有统计学意义，留一法（LOO）验证相关系数（q²）分别为0.613和0.622，交叉验证系数（r²cv）分别为0.617和0.607，常规系数（r²ncv）分别为0.886和0.859。两个模型均通过18种化合物的测试集进行验证，CoMFA和CoMSIA模型的预测相关系数（r²pred）分别为0.714和0.549，令人满意。生成的三维QSAR模型用于预测10种化合物外部数据集的pIC50以进行预测验证，CoMFA模型的常规r²为0.893，CoMSIA模型的常规r²为0.774。我们确定了取代苯并咪唑衍生物中的一些关键特征，例如亲脂性以及分别在苯并咪唑环的2位和5、6、7位的氢键对于良好拮抗活性的重要性。本研究中生成的CoMFA和CoMSIA模型为新化合物的设计提供了有用信息，并有助于预测拮抗活性。
