...
首页> 外文期刊>Oriental Journal of Chemistry: An International Research Journal of Pure & Applied Chemistry >Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-square Support Vector Machine
【24h】

Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-square Support Vector Machine

机译:用偏最小二乘和最小二乘支持向量机研究某些苯甲酰基羧酸衍生物的流动性定量结构性质关系

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of mobilities (m) of benzoaromatic carboxylates. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Also, Dragon software was used to calculate some descriptors such as WIHM and GETAWAY. Modeling of the mobility of benzoaromatic carboxylate derivatives as a function of molecular structures was established by means of the least squares support vector machines (LS-SVM). This model was applied for the prediction of the mobility of benzoaromatic carboxylates, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction (RMSEP) of 3.734, 1.931 and 0.018 for MLR, PLS and LS-SVM, respectively. Results have shown that the introduction of LS-SVM for quantum chemical, WIHM and GETAWAY descriptors drastically enhances the ability of prediction in QSAR studies superior to multiple linear regression (MLR) and partial least squares (PLS).
机译:建议进行定量结构-性质关系(QSPR)研究,以预测苯并芳族羧酸盐的迁移率(m)。从头算理论用于计算一些量子化学描述符,包括每个原子的静电势和局部电荷,HOMO和LUMO能量等。此外,Dragon软件用于计算一些描述符,例如WIHM和GETAWAY。通过最小二乘支持向量机(LS-SVM)建立了苯并芳族羧酸酯衍生物的迁移率随分子结构变化的模型。该模型用于预测苯并芳族羧酸盐的迁移率,而建模过程中没有。结果模型显示出较高的预测能力,MLR,PLS和LS-SVM的预测均方根误差(RMSEP)分别为3.734、1.931和0.018。结果表明,为量子化学,WIHM和GETAWAY描述子引入LS-SVM大大提高了QSAR研究中的预测能力,优于多元线性回归(MLR)和偏最小二乘(PLS)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号