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首页> 外文期刊>Computational Chemistry >Mechanistic Study of Anticancer Activity of Some Known Aminopyrimidoisoquinolinequinones via QSAR Classification Methodology
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Mechanistic Study of Anticancer Activity of Some Known Aminopyrimidoisoquinolinequinones via QSAR Classification Methodology

机译：通过QSAR分类法研究某些已知的氨基嘧啶异喹啉醌的抗癌活性机理

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The impact of lipophilicity as represented by the logarithm of octanol/water partition coefficient (logP), the combined steric/polarizability effect as represented by molar refractivity (MR) and bulk as represented by molar volume (MV) on the biological activity of 29 known aminopyrimidoisoquinolinequinones APIQ were analyzed using quantitative structure activity relationships methodology (QSAR). The activity data chosen was the inhibitory concentration (IC_(50)) against human gastric adenocarcinoma (AGS) cell line. On running regression analysis, the physicochemical parameters and IC50 show very weak correlations as evident by the low values of Pearson Correlation R~(2) (0.1 to 0.2). Since the individual compounds show appreciable activity (ranging from 20 to 0.5 μM), classification was resorted to in order to expose mechanistic nesting subgroups. This was done by clustering data points around various trend lines extracted from the scattered plot relating parameters to activity using R~(2) as an index. The correlation of IC_(50) versus MV was chosen a base of classification owing to higher statistical metrics it yield. This gave five regression lines, each of which is believed to represent a separate mechanistic profile. Additional descriptors were used to consolidate the clustering approach and to give depth to the assumed mechanistic profiles of each cluster.
机译：以辛醇/水分配系数（logP）的对数表示的亲脂性，以摩尔折射率（MR）表示的空间/极化率组合效应和以摩尔体积（MV）表示的体积对29种已知生物活性的影响使用定量结构活性关系方法（QSAR）分析了氨基嘧啶基异喹啉醌APIQ。选择的活性数据是对人胃腺癌（AGS）细胞系的抑制浓度（IC_（50））。在进行回归分析时，理化参数与IC50的相关性非常弱，如Pearson Correlation R〜（2）的值较低（0.1至0.2）所证明。由于单个化合物显示出明显的活性（范围从20到0.5μM），因此分类以暴露机械嵌套亚组。这是通过使用R〜（2）作为指标，对从散点图提取的各种趋势线周围的数据点进行聚类来完成的，这些散点图将参数与活动相关联。由于IC_（50）与MV的相关统计指标较高，因此将其选择为分类的基础。这给出了五条回归线，每条回归线被认为代表了单独的机械轮廓。使用附加的描述符来巩固聚类方法，并为每个聚类假定的机械轮廓提供深度。

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