title: Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides.
abstract: The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.
authors.affiliation: Department of Biosystems, Faculty of Bioscience Engineering, Katholieke Universiteit Leuven - KULeuven, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Goodarzi
authors.affiliation: Department of Electrical Engineering, Federal University of Parana (UFPR), Rua Cel. Francisco Heraclito dos Santos, 100, 81531-980 Curitiba, PR, Brazil; Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Imaculada Conceição, 1155, 80215-901 Curitiba, PR, Brazil.
authors.forename: Leandro dos Santos
authors.initials: Ldos S
authors.lastname: Coelho
authors.affiliation: School of Pharmacy and Pharmacology, University of KwaZulu-Natal, Durban 4001, South Africa.
authors.forename: Bahareh
authors.initials: B
authors.lastname: Honarparvar
authors.affiliation: IMCoDeG (CONICET), Fac. de Tecnología y Cs. Aplicadas, Universidad Nacional de Catamarca, Maximio Victoria 55, Catamarca, Argentina.
authors.forename: Erlinda V
authors.initials: EV
authors.lastname: Ortiz
authors.affiliation: Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (CCT La Plata-CONICET, UNLP), Diag. 113 y 64, Sucursal 4, C.C. 16, 1900 La Plata, Argentina. Electronic address: pabloducho@gmail.com.
authors.forename: Pablo R
authors.initials: PR
authors.lastname: Duchowicz
mesh_terms: D016014:Linear Models; D008956:Models, Chemical; D010575:Pesticides / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D055548:Vapor Pressure
chemical_list: D010575:Pesticides
keywords: Multivariable Linear Regression (MLR); Pesticides; Quantitative Structure-Property Relationships (QSPR); Vapor pressure (VP)