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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >SMILES-based optimal descriptors: QSAR modeling of carcinogenicity by balance of correlations with ideal slopes.
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SMILES-based optimal descriptors: QSAR modeling of carcinogenicity by balance of correlations with ideal slopes.

机译：基于SMILES的最佳描述符：通过与理想斜率之间的相关性平衡，对致癌性进行QSAR建模。

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摘要

Optimal descriptors which are calculated using the simplified molecular input line entry system (SMILES) were utilized to build quantitative structure-activity relationships (QSAR) of carcinogenicity (log TD50). Three schemes of the modeling have been examined: 1. The most traditional "classic" training-test system, i.e., models are built with training set and validated with external test set; 2. The correlation balance, i.e., models are built with preliminary estimation of the predictability of the model with the calibration set (this set plays a role of preliminary test set); and 3. The extended correlation balance that takes into account the slopes of regression lines in plots experimental versus predicted values of carcinogenicity (in ideal, these slopes should be similar). It has been shown that the extended correlation balance with the ideal slopes gives most robust prediction of carcinogenicity for external test set. These models have been built by Monte Carlo method for three splits into subtraining set, calibration set, and test set. The number of the N-nitroso groups (i.e., R1-N(R2)-N=O) in a molecular system has been examined as an additional descriptor.
机译：利用简化的分子输入线输入系统（SMILES）计算的最佳描述子可用于建立致癌性的定量构效关系（QSAR）（log TD50）。检验了三种建模方案：1.最传统的“经典”训练测试系统，即使用训练集构建模型并通过外部测试集进行验证; 2.相关性平衡，即模型是通过对带有校准集的模型的可预测性进行初步估计而建立的（该集合起初步测试集的作用）; 3.扩展的相关性平衡，考虑了试验曲线与致癌性预测值之间的回归线的斜率（理想情况下，这些斜率应相似）。已经表明，具有理想斜率的扩展相关平衡可以为外部测试集提供最可靠的致癌性预测。这些模型是通过蒙特卡洛方法建立的，分为三个部分：训练集，校准集和测试集。已经检查了分子系统中的N-亚硝基基团的数目（即，R 1 -N（R 2）-N ＝ O）作为另外的描述。

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