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Structure–property relationships of aliphatic esters using topological descriptors and backward‐multiple linear regression method
Journal of the Chinese Chemical Society ( IF 1.6 ) Pub Date : 2020-03-27 , DOI: 10.1002/jccs.201900528 Mehdi Rajabi 1 , Fatemeh Shafiei 1
Journal of the Chinese Chemical Society ( IF 1.6 ) Pub Date : 2020-03-27 , DOI: 10.1002/jccs.201900528 Mehdi Rajabi 1 , Fatemeh Shafiei 1
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In the present investigation, quantitative structure–property relationship (QSPR) modeling was carried out on 48 aliphatic esters to develop a robust model for the prediction of thermodynamic properties such as the enthalpy of vaporization at standard condition (∆H °vap kJ mol−1) and normal temperature of boiling points (
K). Multiple linear regression (MLR) and backward (BW) stepwise regression methods were used to select the descriptors derived from the Chemicalize program to give the QSPR models. These models were used to delineate the important descriptors responsible for the properties of the aliphatic esters. The multicollinearity and autocorrelation properties of the descriptors used in the models were tested by calculating the variance inflation factor, Pearson correlation coefficient, and the Durbin–Watson statistics. Leave‐one‐out cross‐validation, leave‐group (fivefold)‐out, and external validation criteria (Q 2F1, Q 2F2, Q 2F3, CCC, R 2m) were proposed to verify the predictive performance of QSPR models derived by BW‐MLR analysis. The predictive ability of the models was found to be satisfactory. Thus, QSPR models derived from this study may be helpful for modeling and designing some new aliphatic esters and predicting their properties.
中文翻译：
使用拓扑描述符和后向多元线性回归方法的脂肪族酯的结构-性质关系
在本研究中，对48种脂族酯进行了定量结构-性质关系（QSPR）建模，以开发用于预测热力学性质（例如标准条件下的汽化焓（∆ H ° vap kJ mol -1）和常温沸点（K）。使用多元线性回归（MLR）和向后（BW）逐步回归方法来选择从Chemicalize程序派生的描述符，以提供QSPR模型。这些模型用于描述负责脂族酯性能的重要描述子。通过计算方差膨胀因子，Pearson相关系数和Durbin-Watson统计量，测试了模型中使用的描述符的多重共线性和自相关属性。留一法交叉验证，留组（五倍）留法和外部验证标准（Q 2 F1，Q 2 F2，Q 2 F3， CCC，R 2 m提出）以验证通过BW‐MLR分析得出的QSPR模型的预测性能。发现模型的预测能力令人满意。因此，从这项研究得出的QSPR模型可能有助于建模和设计一些新的脂族酯并预测其性能。
更新日期：2020-03-27
K). Multiple linear regression (MLR) and backward (BW) stepwise regression methods were used to select the descriptors derived from the Chemicalize program to give the QSPR models. These models were used to delineate the important descriptors responsible for the properties of the aliphatic esters. The multicollinearity and autocorrelation properties of the descriptors used in the models were tested by calculating the variance inflation factor, Pearson correlation coefficient, and the Durbin–Watson statistics. Leave‐one‐out cross‐validation, leave‐group (fivefold)‐out, and external validation criteria (Q 2F1, Q 2F2, Q 2F3, CCC, R 2m) were proposed to verify the predictive performance of QSPR models derived by BW‐MLR analysis. The predictive ability of the models was found to be satisfactory. Thus, QSPR models derived from this study may be helpful for modeling and designing some new aliphatic esters and predicting their properties.
中文翻译：
使用拓扑描述符和后向多元线性回归方法的脂肪族酯的结构-性质关系
在本研究中，对48种脂族酯进行了定量结构-性质关系（QSPR）建模，以开发用于预测热力学性质（例如标准条件下的汽化焓（∆ H ° vap kJ mol -1）和常温沸点（
K）。使用多元线性回归（MLR）和向后（BW）逐步回归方法来选择从Chemicalize程序派生的描述符，以提供QSPR模型。这些模型用于描述负责脂族酯性能的重要描述子。通过计算方差膨胀因子，Pearson相关系数和Durbin-Watson统计量，测试了模型中使用的描述符的多重共线性和自相关属性。留一法交叉验证，留组（五倍）留法和外部验证标准（Q 2 F1，Q 2 F2，Q 2 F3， CCC，R 2 m提出）以验证通过BW‐MLR分析得出的QSPR模型的预测性能。发现模型的预测能力令人满意。因此，从这项研究得出的QSPR模型可能有助于建模和设计一些新的脂族酯并预测其性能。
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