有机化合物介电常数的通用定量结构-性质关系处理方法。
A general QSPR treatment for dielectric constants of organic compounds.
作者信息
Sild Sulev, Karelson Mati
机构信息
Institute of Chemical Physics, University of Tartu, Jakobi Street 2, Tartu, 51014, Estonia.
出版信息
J Chem Inf Comput Sci. 2002 Mar-Apr;42(2):360-7. doi: 10.1021/ci010335f.
Multilinear regression and neural network methods have been used to develop QSPR models for the prediction of the dielectric constant (epsilon) and Kirkwood function (epsilon - 1)/(2epsilon + 1) of organic liquids. Both methods can provide acceptable models for the prediction of these properties. The QSPR models developed from the training set of 155 diverse compounds use theoretical molecular descriptors encoding electronic properties of the molecule and the intermolecular interaction between molecules. The QSPR models for the Kirkwood function appear to be more reliable than the models for the dielectric constant. The average prediction error of the best model for the dielectric constant is 27.0%. The average prediction error of the best model for the Kirkwood function is 4.1%.
多线性回归和神经网络方法已被用于开发定量构效关系（QSPR）模型，以预测有机液体的介电常数（ε）和柯克伍德函数（(ε - 1)/(2ε + 1)）。两种方法都能为这些性质的预测提供可接受的模型。从155种不同化合物的训练集开发的QSPR模型使用了编码分子电子性质和分子间相互作用的理论分子描述符。柯克伍德函数的QSPR模型似乎比介电常数的模型更可靠。介电常数最佳模型的平均预测误差为27.0%。柯克伍德函数最佳模型的平均预测误差为4.1%。
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