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 journalArticlepeer-review

14 Scopus citations


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 languageEnglish (US)
Pages (from-to)3179-3186
Number of pages8
JournalAtmospheric Environment
Issue number26
StatePublished - Aug 1 2010


  • 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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