title: Comparison of in silico models for prediction of mutagenicity.
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.
authors.affiliation: Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy. nazanin.golbamaki@marionegri.it
authors.forename: Nazanin G
authors.initials: NG
authors.lastname: Bakhtyari
authors.forename: Giuseppa
authors.initials: G
authors.lastname: Raitano
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
authors.forename: Todd
authors.initials: T
authors.lastname: Martin
authors.forename: Douglas
authors.initials: D
authors.lastname: Young
mesh_terms: D003198:Computer Simulation; D015386:Hazardous Substances / Q000633:toxicity*; D008956:Models, Chemical; D016296:Mutagenesis; D009152:Mutagenicity Tests; D009153:Mutagens / Q000737:chemistry* / Q000633:toxicity*; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D012984:Software
chemical_list: D015386:Hazardous Substances; D009153:Mutagens