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• W2118728142 abstract "A quantitative structure-property relationship (QSPR) study based on the wavelet neural network (WNN) technique was performed for the prediction of gas chromatography retention indexes of methyl-substituted alkanes produced by insects. In addition to the simple structural descriptors, semi-empirical quantum chemical calculations at the AM1 (Austin Model 1) level were used to find the optimum 3D geometry of the studied molecules and a numbers of descriptors were calculated with HyperChem and Dragon software. A stepwise MLR (Multiple Linear Regression) method was used to select the best descriptors, and the selected descriptors were used as input neurons in a wavelet neural network model. The average relative error was 2.2%." @default.
• W2118728142 created "2016-06-24" @default.
• W2118728142 creator A5000799362 @default.
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• W2118728142 date "2008-08-01" @default.
• W2118728142 modified "2023-09-26" @default.
• W2118728142 title "Prediction of GC Retention Indexes for Insect-Produced Methyl-Substituted Alkanes Using a Wavelet Neural Network" @default.
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• W2118728142 doi "https://doi.org/10.1002/jccs.200800110" @default.
• W2118728142 hasPublicationYear "2008" @default.
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