College of Materials Science and Engineering, Key Laboratory of Green Processing and Functional Textiles of New Textile Materials, Ministry of Education, Wuhan Textile University, Wuhan, China.
J Comput Chem. 2011 Nov 30;32(15):3241-52. doi: 10.1002/jcc.21907. Epub 2011 Aug 12.
A quantitative structure-property relationship (QSPR) study was performed for the prediction of the Setschenow constants (K(salt)) by sodium chloride of organic compounds. The entire set of 101 compounds was randomly divided into a training set of 71 compounds and a test set of 30 compounds. Multiple linear regression, artificial neural network (ANN), and support vector machine (SVM) were utilized to build the linear and nonlinear QSPR models, respectively. The obtained models with four descriptors involved show good predictive ability. The linear model fits the training set with R(2) = 0.8680, while ANN and SVM higher values of R(2) = 0.8898 and 0.9302, respectively. The validation results through the test set indicate that the proposed models are robust and satisfactory. The QSPR study suggests that the molecular lipophilicity is closely related to the Setschenow constants.
进行了一项定量构效关系（QSPR）研究，以通过氯化钠预测有机化合物的 Setschenow 常数（K(salt)）。将全部 101 种化合物随机分为 71 种化合物的训练集和 30 种化合物的测试集。分别使用多元线性回归、人工神经网络（ANN）和支持向量机（SVM）来构建线性和非线性 QSPR 模型。包含四个描述符的获得模型显示出良好的预测能力。线性模型拟合训练集的 R(2) = 0.8680，而 ANN 和 SVM 的 R(2) 值更高，分别为 0.8898 和 0.9302。通过测试集的验证结果表明，所提出的模型是稳健且令人满意的。QSPR 研究表明，分子亲脂性与 Setschenow 常数密切相关。
