Golmohammadi Hassan, Dashtbozorgi Zahra, Acree William E
Department of Chemistry, Shahr-e-Rey Branch, Islamic Azad University, Theran, Iran.
Young Researchers Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran phone/fax: +98 21 77641873.
Mol Inform. 2012 May;31(5):385-97. doi: 10.1002/minf.201200007. Epub 2012 Apr 30.
In this study, a quantitative structureproperty relationship (QSPR) study is developed for the prediction of gas to dimethyl sulfoxide solvation enthalpy (ΔHSolv ) of organic compounds based on molecular descriptors calculated solely from molecular structure considerations. Diverse types of molecular descriptors were calculated to represent the molecular structures of the various compounds studied. Multiple linear regression (MLR) was employed to select an optimal subset of descriptors that have significant contributions to the ΔHSolv overall property. Our investigation revealed that the dependence of physicochemical properties on solvation enthalpy is a nonlinear observable fact and that MLR method is unable to model the solvation enthalpy accurately. It has been observed that support vector machine (SVM) and artificial neural network (ANN) demonstrates better performance compared with MLR. The standard error value of the test set for SVM is 1.731 kJ mol(-1) , while it is 2.303 kJ mol(-1) and 5.146 kJ mol(-1) for ANN and MLR, respectively. The results showed that the calculated ΔHSolv values by SVM were in good agreement with the experimental data, and the performance of the SVM model was superior to those of MLR and ANN ones.
在本研究中，基于仅从分子结构考虑计算得到的分子描述符，开展了定量结构-性质关系（QSPR）研究，以预测有机化合物在二甲基亚砜中的溶解焓（ΔHSolv）。计算了多种类型的分子描述符来表征所研究的各种化合物的分子结构。采用多元线性回归（MLR）来选择对ΔHSolv整体性质有显著贡献的描述符的最优子集。我们的研究表明，物理化学性质对溶解焓的依赖性是一个非线性的可观察事实，并且MLR方法无法准确模拟溶解焓。据观察，与MLR相比，支持向量机（SVM）和人工神经网络（ANN）表现出更好的性能。SVM测试集的标准误差值为1.731 kJ·mol⁻¹，而ANN和MLR的分别为2.303 kJ·mol⁻¹和5.146 kJ·mol⁻¹。结果表明，SVM计算得到的ΔHSolv值与实验数据吻合良好，且SVM模型的性能优于MLR和ANN模型。
