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

Mohammad Goodarzi; Erlinda V. Ortiz; Leandro dos S. Coelho; Pablo R. Duchowicz

doi:10.1016/j.atmosenv.2010.05.025

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

Mohammad Goodarzi, Erlinda V. Ortiz, Leandro dos S. Coelho, Pablo R. Duchowicz

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	3179-3186
Number of pages	8
Journal	Atmospheric Environment
Volume	44
Issue number	26
DOIs	https://doi.org/10.1016/j.atmosenv.2010.05.025
State	Published - Aug 1 2010

Keywords

Artificial neural networks
Dragon molecular descriptors
Henry's law constant
QSPR-QSAR Theory
Replacement method

ASJC Scopus subject areas

Environmental Science(all)
Atmospheric Science

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10.1016/j.atmosenv.2010.05.025

Cite this

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abstract = "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.",

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AU - Ortiz, Erlinda V.

AU - Coelho, Leandro dos S.

AU - Duchowicz, Pablo R.

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

Abstract

Keywords

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