QSPR Modeling of Soil Sorption Coefficients (K OC) of Pesticides Using SPA-ANN and SPA-MLR

Nasser Goudarzi, Mohammad Goodarzi, Mario Cesar Ugulino Araujo, Roberto Kawakami Harrop Galvão

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


A quantitative structure-property relationship (QSPR) study was conducted to predict the adsorption coefficients of some pesticides. The successive projection algorithm feature selection (SPA) strategy was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and adsorption coefficient data was achieved by linear (multiple linear regression; MLR) and nonlinear (artificial neural network; ANN) methods. The QSPR models were validated by cross-validation as well as application of the models to predict the K OC of external set compounds, which did not contribute to model development steps. Both linear and nonlinear methods provided accurate predictions, although more accurate results were obtained by the ANN model. The root-mean-square errors of test set obtained by MLR and ANN models were 0.3705 and 0.2888, respectively.

Original languageEnglish (US)
Pages (from-to)7153-7158
Number of pages6
JournalJournal of Agricultural and Food Chemistry
Issue number15
StatePublished - Aug 12 2009


  • Artificial neural network
  • Quantitative structure-activity relationship
  • Soil sorption coefficients
  • Successive projection algorithm

ASJC Scopus subject areas

  • Chemistry(all)
  • Agricultural and Biological Sciences(all)


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