期刊
JOURNAL OF COLLOID AND INTERFACE SCIENCE
卷 314, 期 2, 页码 665-672出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcis.2007.06.047
关键词
micelle-water partition coefficients; multiple linear regressions; artificial neural network; quantitative structure-property relationship; theoretical molecular descriptor
The micelle-water partition coefficients of 8 1 organic compounds in SIDS solution were predicted by quantitative structure-property relationship method. The multiple linear regression (MLR) and artificial neural network (ANN) techniques were used to build linear and nonlinear model,, respectively. In this work the proposed QSPR models, both by MLR and ANN, contain identical descriptors which are zero order of Kier-Hall index. count of Hydrogen donors site [Zefirovs PC], average valency of a C atom, atomic charge weighted by partial positively charged surface area and minimum one electron reaction index for a C atom. The MLR model gave a root mean square (RMS) of 0.166, 0.25, and 0.289 for training, prediction and test sets, respectively, whereas ANN gave an RMS error of 0.06, 0.21, and 0.20 for training, prediction, and test sets, respectively. Comparison the results of these two methods reveals that those obtained by the ANN model are much better. (c) 2007 Elsevier Inc. All rights reserved.
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