QSAR model for predicting pesticide aquatic toxicity

Paolo Mazzatorta, Martin Smiesko, Elena Lo Piparo, Emilio Benfenati

Research output: Contribution to journalArticlepeer-review

Abstract

A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Finally, various linear and nonlinear regression techniques were used to obtain stable and thoroughly validated QSARs. The final model was developed by a counterpropagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors, namely HACA-2, HOMO-LUMO energy gap, Kier and Hall index, HA dependent HDSA-1, BETA polarizability, FHBCA fractional HBSA, and LogP. The model was extensively tested by the test set (R2 = 0.79), the y-scrambling test, and sensitivity/stability tests.

Original languageEnglish
Pages (from-to)1767-1774
Number of pages8
JournalJournal of Chemical Information and Modeling
Volume45
Issue number6
DOIs
Publication statusPublished - Nov 2005

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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