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QSARINS: A new software for the development, analysis, and validation of QSAR MLR models

Authors

  • Paola Gramatica,

    Corresponding author
    • Department of Theoretical and Applied Sciences, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, Varese, Italy. E-mail: paola.gramatica@uninsubria.it
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  • Nicola Chirico,

    1. Department of Theoretical and Applied Sciences, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, Varese, Italy. E-mail: paola.gramatica@uninsubria.it
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  • Ester Papa,

    1. Department of Theoretical and Applied Sciences, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, Varese, Italy. E-mail: paola.gramatica@uninsubria.it
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  • Stefano Cassani,

    1. Department of Theoretical and Applied Sciences, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, Varese, Italy. E-mail: paola.gramatica@uninsubria.it
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  • Simona Kovarich

    1. Department of Theoretical and Applied Sciences, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, Varese, Italy. E-mail: paola.gramatica@uninsubria.it
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E-mail: paola.gramatica@uninsubria.it

Abstract

QSARINS (QSAR-INSUBRIA) is a new software for the development and validation of multiple linear regression Quantitative Structure-Activity Relationship (QSAR) models by Ordinary Least Squares method and Genetic Algorithm for variable selection. This program is mainly focused on the external validation of QSAR models. Various tools for explorative analysis of the datasets by Principal Component Analysis, prereduction of input molecular descriptors, splitting of datasets in training and prediction sets, detection of outliers and interpolated or extrapolated predictions, internal and external validation by different parameters, consensus modeling and various plots for visualizations are implemented. QSARINS is a user-friendly platform for QSAR modeling in agreement with the OECD Principles and for the analysis of the reliability of the obtained predicted data. The Insubria Persistent Bioaccumulative and Toxic (PBT) Index model for the prediction of the cumulative behavior of new chemicals as PBTs is implemented. Additionally, QSARINS allows the user to validate single models, predeveloped using also different software. © 2013 Wiley Periodicals, Inc.

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