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) |
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Pages (from-to) | 3179-3186 |
Number of pages | 8 |
Journal | Atmospheric Environment |
Volume | 44 |
Issue number | 26 |
DOIs | |
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