Gramatica, P.; Pilutti, P.; Papa, E. Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity. SAR QSAR Environ. Res. 2007, 18, 169–178.

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Gramatica, P.; Pilutti, P.; Papa, E. Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity. SAR QSAR Environ. Res. 2007, 18, 169–178.

QDB archive DOI: 10.15152/QDB.171   DOWNLOAD

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Property logTA100exp: Mutagenicity potency in TA100 without the S9 activation system [log(revertants/nmol)]

Mod4: Full model for mutagenicity of nitrated polycyclic aromatic hydrocarbons

Regression model (regression)

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traintraining480.8140.751

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  • Garcia-Sosa, A. T. Data for: Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity. QsarDB repository, QDB.171. 2015. http://dx.doi.org/10.15152/QDB.171

  • Gramatica, P.; Pilutti, P.; Papa, E. Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity. SAR QSAR Environ. Res. 2007, 18, 169–178. http://dx.doi.org/10.1080/10629360601054388

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Title: Gramatica, P.; Pilutti, P.; Papa, E. Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity. SAR QSAR Environ. Res. 2007, 18, 169–178.
Abstract:Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach.
URI:http://hdl.handle.net/10967/171
http://dx.doi.org/10.15152/QDB.171
Date:2015-09-25


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    Uni. Insubria (Italy), QSAR Research Unit in Environmental Chemistry and Ecotoxicology

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