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Article

QSAR Model for Predicting Pesticide Aquatic Toxicity

Supporting Info

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

Istituto di Ricerche Farmacologiche Mario Negri Milano, Via Eritrea, 62, 20157 Milano, Italy

J. Chem. Inf. Model., 2005, 45 (6), pp 1767–1774

DOI: 10.1021/ci050247l

Publication Date (Web): September 1, 2005

Corresponding author phone: +39-02-39014420; fax: +39-02-39001916; e-mail: lopiparo@marionegri.it.

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.

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Published In Issue November 28, 2005
Received June 17, 2005

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Article

QSAR Model for Predicting Pesticide Aquatic Toxicity

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Recommend & Share

Related Content

Top-Priority Fragment QSAR Approach in Predicting Pesticide Aquatic Toxicity

Statistically Validated QSARs, Based on Theoretical Descriptors, for Modeling Aquatic Toxicity of Organic Chemicals in Pimephales promelas (Fathead Minnow)

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and Ab Initio Methods for Development of Toxicological QSARs

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