Linear and non-linear relationships mapping the Henry's law parameters of organic pesticides

被引：14

作者：

Goodarzi, Mohammad ^{[1
]}

Ortiz, Erlinda V. ^{[2
]}

Coelho, Leandro dos S. ^{[3
]}

Duchowicz, Pablo R. ^{[1
]}

机构：

[1] UNLP, CCT La Plata CONICET, INIFTA, Inst Invest Fisicoquim Teor & Aplicadas, RA-1900 La Plata, Argentina

[2] Univ Natl Catamarca, Fac Tecnol & Ciencias Aplicadas, RA-4700 Catamarca, Argentina

[3] Pontifical Catholic Univ Parana PUCPR, Ind & Syst Engn Grad Program PPGEPS, BR-80215901 Curitiba, Parana, Brazil

来源：

ATMOSPHERIC ENVIRONMENT | 2010年 / 44卷 / 26期

关键词：

QSPR-QSAR Theory; Replacement method; Artificial neural networks; Henry's law constant; Dragon molecular descriptors; STRUCTURE-PROPERTY RELATIONSHIP; AQUEOUS SOLUBILITY; VARIABLE SELECTION; DIVERSE SET; CONSTANT; QSPR; PREDICTION; QSAR; REGRESSION; ALGORITHM;

D O I：

10.1016/j.atmosenv.2010.05.025

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative Structure-Property Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanol-water partition coefficient, and higher lipophilicities would lead to compounds having higher Henry's law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the Levenberg-Marquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：3179 / 3186

页数：8

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