QSAR in Ecotoxicity:  An Overview of Modern Classification Techniques

Paolo Mazzatorta,* Emilio Benfenati, Paola Lorenzini, and Marco Vighi
Istituto di Ricerche Farmacologiche Mario Negri Milano, Via Eritrea, 62, 20157 Milano, Italy, and Universit degli Studi di Milano Bicocca, Dip. di Scienze dell'Ambiente e del Territorio (DISAT), Piazza della Scienza 1, 20126 Milano, Italy
J. Chem. Inf. Comput. Sci., 2004, 44 (1), pp 105–112
DOI: 10.1021/ci034193w
Publication Date (Web): December 18, 2003
Copyright © 2004 American Chemical Society
*

 Corresponding author phone:  +39-02-39014499; fax:  +39-02-39001916; e-mail:  mazzatorta@marionegri.it.

,

 Istituto di Ricerche Farmacologiche “Mario Negri” Milano.

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 Università degli Studi di Milano Bicocca.

Abstract

This study deals with classification for toxicity prediction. Using a data set of 235 pesticides and 153 descriptors, we built several models using seven classification algorithms:  nearest mean classifier, linear discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, soft independent modeling of class analogy, K nearest neighbors classification, classification, and regression tree. The performance of the models was then compared with the classifier, the end-points, the number of descriptor, and the diversity of the data set. Finally, we made a critical analysis of the models and descriptors.

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History

  • Published In Issue January 26, 2004
  • Received September 2, 2003

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