Xia Binbin, Ma Weiping, Zhang Xiaoyun, Fan Botao
Department of Chemistry, Lanzhou University, Lanzhou 730000, Gansu, PR China.
Anal Chim Acta. 2007 Aug 13;598(1):12-8. doi: 10.1016/j.aca.2007.07.016. Epub 2007 Jul 30.
Quantitative structure-retention relationship (QSRR) models have been successfully developed for the prediction of the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 66 organic pollutants. Heuristic method (HM) and radial basis function neural networks (RBFNN) were utilized to construct the linear and non-linear QSRR models, respectively. The optimal QSRR model was developed based on a 6-17-1 radial basis function neural network architecture using molecular descriptors calculated from molecular structure alone. The RBFNN model gave a correlation coefficient (R2) of 0.8464 and root-mean-square error (RMSE) of 0.1925 for the test set. This paper provided a useful model for the predicting the log k of other organic compounds when experiment data are unknown.
已成功开发出定量结构保留关系（QSRR）模型，用于预测66种有机污染物在生物分配胶束色谱法（BMC）中的保留因子（log k）。分别采用启发式方法（HM）和径向基函数神经网络（RBFNN）构建线性和非线性QSRR模型。基于仅从分子结构计算得到的分子描述符，采用6-17-1径向基函数神经网络结构开发了最优QSRR模型。RBFNN模型对测试集的相关系数（R2）为0.8464，均方根误差（RMSE）为0.1925。本文提供了一个有用的模型，用于在实验数据未知时预测其他有机化合物的log k。
