胆固醇酯转运蛋白抑制剂的3D-QSAR模型

利用VolSurf参数和比较分子场分析(CoMFA)方法对N,N-二取代三氟-3-氨基-2-丙醇衍生物类胆固醇酯转运蛋白抑制剂进行了三维定量构效关系(3D-QSAR)模型研究, 均得到较好的结果, 训练集模型具有良好的预测能力. VolSurf参数分析表明抑制活性高的分子必须具有合适的亲水性、多的氢键给体和少的氢键受体; 在一定范围内, 分子量大、表面光滑且非球性高的分子抑制活性高; 高疏水性以及质量中心与疏水区中心的高不平衡性对活性是不利因素. CoMFA结果表明, 立体作用对活性的影响较静电作用稍强, N-苯基取代基苯氧基的间位体积大且正电性强的基团对活性有利, N-苄基取代基的间位体积大且合适的电负性对活性有利, 而苄基的对位立体位阻的增加则对活性不利. VolSurf参数提供了分子整体性质信息, CoMFA提供了取代基信息, 两者互为补充, 对该类抑制剂新化合物的设计具有指导意义.     

The comparative molecular surface analysis (CoMSA) with modified uniformative variable elimination-PLS (UVE-PLS) method: application to the steroids binding the aromatase enzyme

The application of the CoMSA method to analyze 3D QSAR of 50 steroid aromatase inhibitors is described. The 3D QSAR model obtained, reaching a value of cross-validated q(2) = 0.96 (s = 0.31), significantly outperforms those reported in the literature for the CoMFA or CoSA (CoSASA). It is shown that the Uniformative Variable Elimination UVE-PLS or modified iterative UVE procedure (IVE-PLS) can be used for indicating the regions contributing to the binding activity. Thus, after separating the series into two groups of the training and test molecules quite correct external predictions result from the processing of the training set. We proved that the procedure of the data elimination provides stable results, if tested in 50 random runs of the IVE-PLS-CoMSA with different training/test sets. Depending upon the procedure used the quality of the predictions for 25 test molecules is given by SDEP = sum(y(pred)-y(obs))(2)/n)(1/2) = 0.321 - 0.782.     

二肽肽酶IV抑制剂的三维定量构效关系研究

二肽肽酶IV是一类用于治疗II型糖尿病具有潜在价值的关键酶, 很多此类酶的抑制剂用于处理此病具有相当好的有效性. 一系列N-取代的甘氨酰氰基吡咯烷衍生物对于二肽肽酶具有高的活性和选择性. 我们使用比较分子力场分析方法建立DPP-IV抑制剂——N-取代的甘氨酰氰基吡咯衍生物的三维定量构效关系, 该模型为设计用于治疗II型糖尿病的高效DPP-IV抑制剂提供结构信息. CoMFA模型的交叉验证相关系数q²＝0.575, 非交叉验证相关系数r²＝0.981, 绝对误差S＝0.184, F_9.68＝388.5. 使用七个预测集检验了模型的预测能力. 所得的模型解释了已有的构效关系, 并对同类化合物有较好的预测能力, 该模型可用于指导新型的DPP-IV抑制剂的设计与优化.     

CoMFA Model and Molecular Design of Anti-excitatory Activity for Benzodiazepinooxazole Derivatives against Mice

A 3D-QSAR study was conducted to analyze the anti-excitatory activity(p E) of benzodiazepinooxazole derivatives to mice by the comparative molecular field analysis(CoMFA) method. Among the 54 active molecules, a training set of 46 compounds was randomly selected to construct the CoMFA model; the remaining compounds, together with template molecule(No. 54) and two newly designed molecules constitute a test set of 17 compounds to validate the model. The obtained cross-validation coefficient(R_(cv)~2), the non-cross validation coefficient(R~2), and the test value F of the CoMFA model for training set are 0.516, 0.899, and 57.57,respectively. The model was used to predict the activities of all compounds in the training and testing sets, and the results indicated that the model had good correlation, strong stability and good predictability. Based on the 3D contour maps, eight novel benzodiazepinooxazole derivatives with higher anti-excitatory activity were designed.However, the effectiveness of these novel benzodiazepinooxazole derivatives is still needed to be verified by the experimental results.     

3D-QSAR Analysis of Naphthyltriazole(Lesinurad) Analogs as Potent Inhibitors of Urate Transporter 1

To obtain useful information for identifying inhibitors of urate transporter 1(URAT1), three-dimensional quantitative structure-activity relationship(3 D-QSAR) analysis was conducted for a series of lesinurad analogs via Topomer comparative molecular field analysis(CoMFA). A 3 D-QSAR model was established using a training set of 51 compounds and externally validated with a test set of 17 compounds. The Topomer CoMFA model obtained(q^2 = 0.976, r2 = 0.990) was robust and satisfactory. Subsequently, seven compounds with significant URAT1 inhibitory activity were designed according to the contour maps produced by the Topomer CoMFA model.     

3D-QSAR Studies on a Series of Indoleamide Derivatives as Antiplasmodial Drugs

In this study, three-dimensional quantitative structure-activity relationship(3D-QSAR) was studied for the antiplasmodial activity of a series of novel indoleamide derivatives by comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(Co MSIA). 3D-QSAR model was established by a training set of 20 compounds and was externally validated by a test set of 4 compounds. The best prediction(Q~2 = 0.593 and 0.527, R~2 = 0.990 and 0.953, r_(pred)~2 = 0.967 and 0.962 for CoMFA and CoMSIA) was obtained according to CoMFA and CoMSIA. Those parameters indicated the model was reliable and predictable. We designed several molecules with high activities according to the contour maps produced by the CoMFA and CoMSIA models.     

3D-QSAR Analysis of a Series of 1,2,3-Triazole-chromenone Derivatives as an Acetylcholinesterase Inhibitor against Alzheimer's Disease

Chromenones have attracted much attention since they are excellent acetylcholinesterase inhibitor(AChEi). The 1,2,3-triazoles are multifunctional anti-acetylcholinesterase(AChE) agents. In this paper, we report the three-dimensional quantitative structure-activity relationship(3D-QSAR) study of 25 1,2,3-triazolechromenone derivatives based comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA). To construct CoMFA and CoMSIA models, the 25 active molecules were randomly divided into the training and test sets. The obtained cross-validation Q~2 of the CoMFA model, the coefficient of non-cross-validation R~2, and the test value F are 0.597, 0.994, and 396.726, respectively. The cross-validation Q~2 of the CoMSIA model, the coefficient of the non-cross-validation R~2, and the test value F are 0.721, 0.979, and 131.107, respectively. The predictive correlation coefficient(r_(pred)~2) is 0.728 for CoMFA and 0.805 for CoMSIA, which verifies that the model is predictable. Based on the potential maps of CoMFA and CoMSIA, a library containing a set of potent AChEi was designed. The inhibitory potential of the compounds in this library was found to be greater than the inhibitory potential of the most active compounds in the data set. The results obtained from this study laid the foundation for the development of effective drugs for AChEi.     

毒蕈碱受体激动剂的三维定量构效关系研究

采用比较分子场分析法(CoMFA)研究了55个四氢吡啶类毒蕈碱受体激动剂的三维定量构效关系(3D-QSAR), 建立了具有较强预测能力的3D-QSAR模型. 所得模型的交叉验证相关系数(q²)为0.507, 常规相关系数(R²)为0.982 , 标准方差为0.218, 说明系列化合物分子周围立体场和静电场的分布与生物活性间存在良好的相关性. 模型不仅很好地预测了训练集和测试集化合物的活性, 而且为设计活性更高的受体激动剂提供了理论依据.