title: Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines.
abstract: A quantitative structure-property relationship (QSPR) study is suggested for the prediction of toxicity (IGC50) of nitrobenzenes. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the IGC50 of nitrobenzenes as a function of molecular structures was established by means of the least squares support vector machines (LS-SVM). This model was applied for the prediction of the toxicity (IGC50) of nitrobenzenes, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.0049 for LS-SVM. Results have shown that the introduction of LS-SVM for quantum chemical descriptors drastically enhances the ability of prediction in QSAR studies superior to multiple linear regression and partial least squares.
authors.affiliation: Department of Chemistry, Faculty of Sciences, Azad University of Arak, Arak, Iran. ali.niazi@gmail.com
authors.forename: Ali
authors.initials: A
authors.lastname: Niazi
authors.forename: Saeed
authors.initials: S
authors.lastname: Jameh-Bozorghi
authors.forename: Davood
authors.initials: D
authors.lastname: Nori-Shargh
mesh_terms: D004784:Environmental Monitoring / Q000379:methods; D015386:Hazardous Substances; D020128:Inhibitory Concentration 50; D016018:Least-Squares Analysis; D016014:Linear Models; D008956:Models, Chemical; D015233:Models, Statistical; D008962:Models, Theoretical; D009578:Nitrobenzenes / Q000032:analysis* / Q000737:chemistry / Q000633:toxicity; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D011789:Quantum Theory; D015203:Reproducibility of Results; D012984:Software
chemical_list: D015386:Hazardous Substances; D009578:Nitrobenzenes