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Prediction of GC Retention Indexes for Insect‐Produced Methyl‐Substituted Alkanes Using a Wavelet Neural Network
Author(s) -
Atabati Morteza,
Zarei Kobra
Publication year - 2008
Publication title -
journal of the chinese chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 45
eISSN - 2192-6549
pISSN - 0009-4536
DOI - 10.1002/jccs.200800110
Subject(s) - chemistry , molecular descriptor , quantitative structure–activity relationship , artificial neural network , linear regression , biological system , quantum chemical , stepwise regression , pattern recognition (psychology) , kovats retention index , wavelet , regression analysis , artificial intelligence , regression , gas chromatography , molecule , chromatography , statistics , stereochemistry , organic chemistry , mathematics , computer science , biology
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%.