Modarresi H, Modarress H, Dearden J C
Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, Tehran, Iran.
SAR QSAR Environ Res. 2005 Oct;16(5):461-82. doi: 10.1080/10659360500319869.
In this study, a quantitative structure-property relationship (QSPR) model for the prediction of Henry's law constants of aliphatic hydrocarbons in air-water system has been developed, based on a data-set of 189 compounds. The well-known linear thermodynamic relation between the logarithm of Henry's law constant and solvation free energy has been used for developing the model. It is emphasised that the solvent-accessible surface area (SASA) descriptor is not adequate for predicting the solvation free energy of a wide range of aliphatic hydrocarbons; there are many compounds that have the same solvent-accessible surface area with different solvation free energy. Therefore, we have introduced cavity ovality as a good descriptor of molecular cavity shape factor. The root mean square error (RMSE) of the QSPR regression model based on SASA improves from 0.40 to 0.22 by introducing the cavity ovality descriptor. The QSPR linear ovality model has good statistical parameters (r(2) = 0.90). To emphasise the significant effect of the new descriptor, a non-linear neural network model with only two nodes in the hidden layer was developed, and also yielded a RMSE of 0.22.
在本研究中,基于189种化合物的数据集,开发了一种用于预测空气-水体系中脂肪族烃亨利定律常数的定量结构-性质关系(QSPR)模型。亨利定律常数的对数与溶剂化自由能之间著名的线性热力学关系被用于构建该模型。需要强调的是,溶剂可及表面积(SASA)描述符不足以预测多种脂肪族烃的溶剂化自由能;有许多化合物具有相同的溶剂可及表面积,但溶剂化自由能不同。因此,我们引入了空腔椭圆率作为分子空腔形状因子的良好描述符。通过引入空腔椭圆率描述符,基于SASA的QSPR回归模型的均方根误差(RMSE)从0.40降至0.22。QSPR线性椭圆率模型具有良好的统计参数(r² = 0.90)。为了强调新描述符的显著影响,开发了一种在隐藏层仅具有两个节点的非线性神经网络模型,其RMSE也为0.22。