title: New Quantitative Structure-Activity Relationship Models Improve Predictability of Ames Mutagenicity for Aromatic Azo Compounds.
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.
authors.affiliation: *Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, via La Masa 19, Milano 20156, Italy serena.manganelli@marionegri.it.
authors.forename: Serena
authors.initials: S
authors.lastname: Manganelli
authors.affiliation: *Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, via La Masa 19, Milano 20156, Italy.
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
authors.affiliation: *Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, via La Masa 19, Milano 20156, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Manganaro
authors.affiliation: Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, Ontario, Canada.
authors.forename: Sunil
authors.initials: S
authors.lastname: Kulkarni
authors.affiliation: Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, Ontario, Canada.
authors.forename: Tara S
authors.initials: TS
authors.lastname: Barton-Maclaren
authors.affiliation: Division of Genetics & Mutagenesis National Institute of Health Sciences 1-18-1 Kamiyoga, Setagaya-Ku, Tokyo 158-8501, Japan.
authors.forename: Masamitsu
authors.initials: M
authors.lastname: Honma
mesh_terms: D000465:Algorithms; D001391:Azo Compounds / Q000633:toxicity*; D008956:Models, Chemical; D009152:Mutagenicity Tests; D021281:Quantitative Structure-Activity Relationship; D012486:Salmonella typhimurium / Q000235:genetics
chemical_list: D001391:Azo Compounds
keywords: CORAL; QSAR; aromatic azo compounds; k-NN