Comparison of in silico models for prediction of mutagenicity

Nazanin G. Bakhtyari, Giuseppa Raitano, Emilio Benfenati, Todd Martin, Douglas Young

Research output: Contribution to journalArticlepeer-review

Abstract

Using a dataset with more than 6000 compounds, the performance of eight quantitative structure activity relationships (QSAR) models was evaluated: ACD/Tox Suite, Absorption, Distribution, Metabolism, Elimination, and Toxicity of chemical substances (ADMET) predictor, Derek, Toxicity Estimation Software Tool (T.E.S.T.), TOxicity Prediction by Komputer Assisted Technology (TOPKAT), Toxtree, CEASAR, and SARpy (SAR in python). In general, the results showed a high level of performance. To have a realistic estimate of the predictive ability, the results for chemicals inside and outside the training set for each model were considered. The effect of applicability domain tools (when available) on the prediction accuracy was also evaluated. The predictive tools included QSAR models, knowledge-based systems, and a combination of both methods. Models based on statistical QSAR methods gave better results.

Original languageEnglish
Pages (from-to)45-66
Number of pages22
JournalJournal of Environmental Science and Health - Part C Environmental Carcinogenesis and Ecotoxicology Reviews
Volume31
Issue number1
DOIs
Publication statusPublished - Jan 1 2013

Keywords

  • Ames test
  • comparative study
  • expert systems
  • in silico
  • mutagenicity
  • prediction
  • quantitative structure-activity relation

ASJC Scopus subject areas

  • Health, Toxicology and Mutagenesis
  • Cancer Research

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