Four-dimensional quantitative structure-activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists
Four-dimensional quantitative structure-activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists
复制标题
DOI：
10.1021/ci980093s
复制
发表时间：
1998-09-01
期刊：
影响因子：
--
通讯作者：
de Alencastro, RB
中科院分区：
文献类型：
--
作者：
Albuquerque, MG;Hopfinger, AJ;de Alencastro, RB
采用四维定量构效关系（4D-QSAR）方法，对39个（训练集29个，测试集10个）苯间菲7-氧杂双环[2.2.1]庚烷恶唑类血栓烷A（2）（TXA（2））受体拮抗剂进行了研究。每种类似物的2000个构象被采样以从100 000个轨迹状态的分子动力学模拟（MDS）生成构象能量分布（CEP）。每种构象被放置在一个网格单元格中，用于六个试验比对中的每一个。考虑1埃和2埃的立方网格单元尺寸。每种化合物的原子的7种pharmacophoric基团类别中的每一种计算每个网格单元的占据频率。这些网格单元占用描述符（GCODs），然后作为独立的变量，在构建三维（3D）的QSAR模型后，数据简化。数据缩减的类型包括不进行缩减;基于个体GCOD与活动的相关性进行缩减，以及从GCOD总体的最小方差约束进行缩减。3D-QSAR模型的生成和评估的计划，结合遗传算法（GA）优化与偏最小二乘（PLS）回归。采用留一法交叉验证方法对3D-QSAR模型进行评价。交叉验证相关系数Q（2）范围为0.27 - 0.86。模型不是偶然相关的，因为产生并评估了乱序数据集（Q（2）= 0.25-0.37）。利用1和2埃网格单元尺寸晶格的GCOD得到的最佳模型构建了复合3D-QSAR模型。3D-QSAR模型提供了详细的3D药效团要求，即高TXA（2）抑制活性所需的原子类型和相应位置。还指定了不应被活性抑制剂占据的空间中的特定位点。在训练集中的化合物的GCOD措施允许参考点，药效团网站可以提供最大的提高抑制活性相对于现有的类似物。
A series of 39 (a training set of 29 and a test set of 10) interphenylene 7-oxabicyclo[2.2.1]heptane oxazole thromboxane A(2) (TXA(2)) receptor antagonists were studied using four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis. Two thousand conformations of each analogue were sampled to generate a conformational energy profile (CEP) from a molecular dynamic simulation (MDS) of 100 000 trajectory states. Each conformation was placed in a grid cell lattice for each of six trial alignments. Cubic grid cell sizes of 1 and 2 Angstrom were considered. The frequency of occupation of each grid cell was computed for each of seven types of pharmcacophoric group classes of atoms of each compound. These grid cell occupancy descriptors (GCODs) were then used as independent variables in constructing three-dimensional (3D)-QSAR models after data reduction. The types of data reduction included doing no reducing; reduction based on individual GCOD correlation with activity, and reduction from minimum variance constraints over the GCOD population. The 3D-QSAR models were generated and evaluated by a scheme that combines a genetic algorithm (GA) optimization with partial least squares (PLS) regression. The 3D-QSAR models were evaluated by cross-validation using the leave-one-out technique. The cross-validated correlation coefficient, Q(2), ranged from 0.27 to 0.86. The models are not from chance correlation because a scrambled data set Was generated and evaluated (Q(2) = 0.25-0.37). A composite 3D-QSAR model was constructed using the best models derived from GCODs of both 1 and 2 Angstrom grid cell size lattices. The 3D-QSAR models provide detailed 3D pharmacophore requirements in terms of atom types and corresponding locations needed for high TXA(2) inhibition activity. Specific sites in space that should not be occupied by an active inhibitor are also specified. The GCOD measures for the compounds in the training set permit reference points regarding which pharmacophore sites can provide the largest boosts in inhibition activity relative to the existing analogues.