使用偏最小二乘回归预测溶解度参数。

Prediction of solubility parameters using partial least square regression.

作者信息

Tantishaiyakul Vimon, Worakul Nimit, Wongpoowarak Wibul

机构信息

Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Prince of Songkla University, Hat-Yai, Songkhla 90112, Thailand.

出版信息

Int J Pharm. 2006 Nov 15;325(1-2):8-14. doi: 10.1016/j.ijpharm.2006.06.009. Epub 2006 Jun 9.

Abstract

The total solubility parameter (delta) values were effectively predicted by using computed molecular descriptors and multivariate partial least squares (PLS) statistics. The molecular descriptors in the derived models included heat of formation, dipole moment, molar refractivity, solvent-accessible surface area (SA), surface-bounded molecular volume (SV), unsaturated index (Ui), and hydrophilic index (Hy). The values of these descriptors were computed by the use of HyperChem 7.5, QSPR Properties module in HyperChem 7.5, and Dragon Web version. The other two descriptors, hydrogen bonding donor (HD), and hydrogen bond-forming ability (HB) were also included in the models. The final reduced model of the whole data set had R(2) of 0.853, Q(2) of 0.813, root mean squared error from the cross-validation of the training set (RMSEcv(tr)) of 2.096 and RMSE of calibration (RMSE(tr)) of 1.857. No outlier was observed from this data set of 51 diverse compounds. Additionally, the predictive power of the developed model was comparable to the well recognized systems of Hansen, van Krevelen and Hoftyzer, and Hoy.

摘要

通过使用计算得到的分子描述符和多元偏最小二乘法(PLS)统计有效地预测了总溶解度参数(δ)值。导出模型中的分子描述符包括生成热、偶极矩、摩尔折射率、溶剂可及表面积(SA)、表面结合分子体积(SV)、不饱和指数(Ui)和亲水指数(Hy)。这些描述符的值通过使用HyperChem 7.5、HyperChem 7.5中的QSPR属性模块以及Dragon网络版进行计算。模型中还包括另外两个描述符,即氢键供体(HD)和氢键形成能力(HB)。整个数据集的最终简化模型的R(2)为0.853,Q(2)为0.813,训练集交叉验证的均方根误差(RMSEcv(tr))为2.096,校准均方根误差(RMSE(tr))为1.857。在这51种不同化合物的数据集中未观察到异常值。此外,所开发模型的预测能力与公认的Hansen、van Krevelen和Hoftyzer以及Hoy体系相当。

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