- 作者: Morteza Atabati* and Kobra Zarei
- 作者服務機構: Department of Chemistry, Damghan University of Basic Sciences, Damghan, Iran
- 中文摘要:
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 methylsubstituted
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%. - 英文摘要: --
- 中文關鍵字: Methyl-substituted alkanes; Insect; Gas chromatography; QSPR; Wavelet neural network.
- 英文關鍵字: --