QSAR in Ecotoxicity: An Overview of Modern Classification Techniques

Paolo Mazzatorta, Emilio Benfenati, Paola Lorenzini, Marco Vighi

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)105-112
Number of pages8
JournalJournal of Chemical Information and Computer Sciences
Issue number1
Publication statusPublished - Jan 2004

ASJC Scopus subject areas

  • Chemistry(all)
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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