A genetic programming-based QSPR model for predicting solubility parameters of polymers

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@Article{Koc:2015:CILS,

• author = "Dilek Imren Koc and Mehmet Levent Koc",
• title = "A genetic programming-based {QSPR} model for predicting solubility parameters of polymers",
• journal = "Chemometrics and Intelligent Laboratory Systems",
• volume = "144",
• pages = "122--127",
• year = "2015",
• ISSN = "0169-7439",
• DOI = "doi:10.1016/j.chemolab.2015.04.005",
• URL = "http://www.sciencedirect.com/science/article/pii/S0169743915000878",
• abstract = "In this study, linear and nonlinear quantitative structure-property relationship (QSPR) models, respectively called the multiple linear regression based QSPR (MLR-QSPR) model and the genetic programming based QSPR (GP-QSPR) model, were built to predict the solubility parameters of polymers with structure -(C1H2-C2R3R4)-, as function of some constitutional, topological and quantum chemical descriptors. The results from the internal validation analysis indicated that the GP-QSPR model has better goodness of fit statistics. The external and overall validation measures also confirmed that the GP-QSPR model significantly outperforms the MLR-QSPR model in terms of some performance metrics over the same testing data set, and that genetic programming has good potential to obtain more accurate models in QSPR studies.",
• keywords = "genetic algorithms, genetic programming, Solubility parameter, Polymers, Linear regression, QSPR",

}

Genetic Programming entries for Dilek Imren Koc Mehmet Levent Koc

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