New quantitative structure-activity relationship models improve predictability of ames mutagenicity for aromatic AZO compounds

Serena Manganelli, Emilio Benfenati, Alberto Manganaro, Sunil Kulkarni, Tara S. Barton-Maclaren, Masamitsu Honma

Research output: Contribution to journalArticlepeer-review

Abstract

Existing Quantitative Structure-Activity Relationship (QSAR) models have limited predictive capabilities for aromatic azo compounds. In this study, 2 new models were built to predict Ames mutagenicity of this class of compounds. The first one made use of descriptors based on simplified molecular input-line entry system (SMILES), calculated with the CORAL software. The second model was based on the k-nearest neighbors algorithm. The statistical quality of the predictions from single models was satisfactory. The performance further improved when the predictions from these models were combined. The prediction results from other QSAR models for mutagenicity were also evaluated. Most of the existing models were found to be good at finding toxic compounds but resulted in many false positive predictions. The 2 new models specific for this class of compounds avoid this problem thanks to a larger set of related compounds as training set and improved algorithms.

Original languageEnglish
Article numberkfw125
Pages (from-to)316-326
Number of pages11
JournalToxicological Sciences
Volume153
Issue number2
DOIs
Publication statusPublished - Oct 1 2016

Keywords

  • Aromatic azo compounds
  • CORAL
  • K-NN
  • QSAR

ASJC Scopus subject areas

  • Toxicology

Fingerprint

Dive into the research topics of 'New quantitative structure-activity relationship models improve predictability of ames mutagenicity for aromatic AZO compounds'. Together they form a unique fingerprint.

Cite this