title: QSAR model for predicting pesticide aquatic toxicity.
abstract: A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Finally, various linear and nonlinear regression techniques were used to obtain stable and thoroughly validated QSARs. The final model was developed by a counterpropagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors, namely HACA-2, HOMO-LUMO energy gap, Kier and Hall index, HA dependent HDSA-1, BETA polarizability, FHBCA fractional HBSA, and LogP. The model was extensively tested by the test set (R2= 0.79), the y-scrambling test, and sensitivity/stability tests.
authors.affiliation: Istituto di Ricerche Farmacologiche Mario Negri Milano, Via Eritrea, 62, 20157 Milano, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Mazzatorta
authors.forename: Martin
authors.initials: M
authors.lastname: Smiesko
authors.forename: Elena
authors.initials: E
authors.lastname: Lo Piparo
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
mesh_terms: D000465:Algorithms; D007928:Lethal Dose 50; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010575:Pesticides / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D015203:Reproducibility of Results; D018570:Risk Assessment; D013816:Thermodynamics; D014481:United States; D014484:United States Environmental Protection Agency; D014874:Water Pollutants, Chemical / Q000633:toxicity*
chemical_list: D010575:Pesticides; D014874:Water Pollutants, Chemical