遗传算法在烷基硝基苯酚类化合物的QSRR中的应用

QSRR of alkyl-nitrophenols in gas chromatography by genetic algorithms

利用分子力学和量子化学方法计算出烷基硝基苯酚类化合物的分子结构描述参数,用遗传算法建立化合物在不同极性色谱柱上的QSRR模型。烷基硝基苯酚类化合物在不同极性色谱柱上的气相色谱保留指数与其分子结构描述参数之间具有较好的线性关系。所建立的QSRR模型与多元线性回归方法建立的模型相比,预测此类化合物的色谱保留值时具有更好的稳定性和准确性。

Solute-related parameters of the Alkyl-nitrophenols were calculated by molecular mechanics and quantum chemical methods. The quantitative structure retention relationships （QSRR） of the Alkyl-nitrophenols on different polar stationary phases were set by the Genetic Algorithms method. The results showed that there is an excellent linear relationship between solute-related parameters and retention indexes （RI）. The QSRR models have better stability and accuracy for prediction of retention indexes of Alkyl-nitrophenols on the different polar stationary phases than those by the multiple linear regression method.