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Statistically Validated QSARs, Based on Theoretical Descriptors, for Modeling Aquatic Toxicity of Organic Chemicals in Pimephales promelas (Fathead Minnow)
journal contribution
posted on 2005-09-26, 00:00 authored by Ester Papa, Fulvio Villa, Paola GramaticaThe use of Quantitative Structure−Activity Relationships in assessing the potential negative effects of
chemicals plays an important role in ecotoxicology. (LC50)96h in Pimephales promelas (Duluth database) is
widely modeled as an aquatic toxicity end-point. The object of this study was to compare different molecular
descriptors in the development of new statistically validated QSAR models to predict the aquatic toxicity
of chemicals classified according to their MOA and in a unique general model. The applied multiple linear
regression approach (ordinary least squares) is based on theoretical molecular descriptor variety (1D, 2D,
and 3D, from DRAGON package, and some calculated logP). The best combination of modeling descriptors
was selected by the Genetic Algorithm-Variable Subset Selection procedure. The robustness and the predictive
performance of the proposed models was verified using both internal (cross-validation by LOO, bootstrap,
Y-scrambling) and external statistical validations (by splitting the original data set into training and validation
sets by Kohonen-artificial neural networks (K-ANN)). The model applicability domain (AD) was checked
by the leverage approach to verify prediction reliability.
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DRAGON packagePimephales promelasModeling Aquatic ToxicityFathead Minnow96 h2 DDuluth databaseregression approachValidated QSARsvalidation setsOrganic ChemicalsLC 50QSAR modelsMOAtoxicityleverage approach3 DPimephales p romelasLOOmodeling descriptorsTheoretical Descriptorschemicalprediction reliabilityADmodel applicability domain