Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide

Authors: R Tabaraki, T Khayamian, Ali A Ensafi

Publication date: 2006/9/30

Journal name: Journal of Molecular Graphics and Modelling

Volume: 25

Pages: 46-54

Publisher: Elsevier

A wavelet neural network (WNN) model in quantitative structure property relationship
(QSPR) was developed for predicting solubility of 25 anthraquinone dyes in supercritical
carbon dioxide over a wide range of pressures (70–770bar) and temperatures (291–423K).
A large number of descriptors were calculated with Dragon software and a subset of
calculated descriptors was selected from 18 classes of Dragon descriptors with a stepwise
multiple linear regression (MLR) as a feature selection technique. Six calculated and two ...