title: [Research on QSPR for n-octanol-water partition coefficients of organic compounds based on genetic algorithms-support vector machine and genetic algorithms-radial basis function neural networks].
abstract: A modified method to develop quantitative structure-property relationship (QSPR) models of organic compounds was proposed based on genetic algorithm (GA) and support vector machine (SVM) (GA-SVM). GA was used to perform the variable selection, and SVM was used to construct QSPR models. GA-SVM was applied to develop the QSPR models for n-octanol-water partition coefficients ( Kow) of 38 typical organic compounds in food industry. 5 descriptors (molecular weights, Hansen polarity, boiling point, percent oxygen and percent hydrogen) were selected in the QSPR model. The coefficient of multiple determination (R2), the sum of squares due to error (SSE) and the root mean squared error (RMSE) values between the measured values and predicted values of the model developed by GA-SVM are 0.999, 0.048 and 0.036, respectively, indicating good predictive capability for lgKow values of these organic compounds. Based on leave-one-out cross validation, the QSPR model constructed by GA-SVM showed good robustness (SSE = 0.295, RMSE = 0.089, R2 = 0.995). Moreover, the models developed by GA-SVM were compared with the models constructed by genetic algorithm-radial basis function neural network (GA-RBFNN) and linear method. The models constructed by GA-SVM show the optimal predictive capability and robustness in the comparison, which illustrates GA-SVM is the optimal method for developing QSPR models for lgKow values of these organic compounds.
authors.affiliation: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China. qijun@mail.bnu.edu.cn
authors.forename: Jun
authors.initials: J
authors.lastname: Qi
authors.forename: Jun-Feng
authors.initials: JF
authors.lastname: Niu
authors.forename: Li-Li
authors.initials: LL
authors.lastname: Wang
mesh_terms: D020003:1-Octanol / Q000032:analysis / Q000737:chemistry*; D000465:Algorithms; D005506:Food Contamination / Q000032:analysis; D018857:Food Packaging; D008956:Models, Chemical; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D014867:Water / Q000737:chemistry*
chemical_list: D014867:Water; D020003:1-Octanol