QSPR Study on the prediction of ionization potential of various organic compounds by heuristic method and radial basis function neural network

机译：启发式方法和径向基函数神经网络预测各种有机化合物电离势的QSPR研究

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Quantitative structure-property relationship study was performed for the prediction of ionization potential (IP) of some organic compounds. Heuristic method (HM) was used to select the most appropriate molecular descriptors. Stepwise multiple linear regression (MLR) and nonlinear radial basis function neural network (RBFNN) were used to build the models. The statistical parameters provided by the MLR model were R² = 0.943; F = 953.469; RMS = 0.1797 for the training set, and R² = 0.952; F = 827.658; RMS = 0.1687 for the external test set. The RBFNN model gave better results: R² = 0.961; F = 4306.030; RMS = 0.1486 for the training set and R² = 0.955; F = 891.009; RMS = 0.1654 for test set. The predicted results were in good agreement with the experimental values.

机译：进行了定量结构-性质关系研究，以预测某些有机化合物的电离势（IP）。启发式方法（HM）用于选择最合适的分子描述符。使用逐步多元线性回归（MLR）和非线性径向基函数神经网络（RBFNN）来建立模型。 MLR模型提供的统计参数为R ^{2 = 0.943; F = 953.469;训练集的RMS = 0.1797，R ^{2 = 0.952; F = 827.658;外部测试装置的RMS = 0.1687。 RBFNN模型给出了更好的结果：R ^{2 = 0.961; F = 4306.030;训练集的RMS = 0.1486，R ^{2 = 0.955; F = 891.009;测试装置的RMS = 0.1654。预测结果与实验值吻合良好。}}}}

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《2011 Seventh International Conference on Natural Computation》|2011年|p.199-202|共4页
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Tian Lihua; Liu Huitao; Luan Feng; Gao Yuan;
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中图分类理论、方法;
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heuristic method; ionization potential; quantitative structure- property relationship; radial basis function neural network; stepwise multiple linear regression;

机译：启发式方法电离势定量构效关系径向基函数神经网络逐步多元线性回归;

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QSPR Study on the prediction of ionization potential of various organic compounds by heuristic method and radial basis function neural network

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