Prediction of critical properties of sulfur-containing compounds: New QSPR models

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@Article{GHOMISHEH:2020:JMGM,

• author = "Zahra Ghomisheh and Ali Ebrahimpoor Gorji and Mohammad Amin Sobati",
• title = "Prediction of critical properties of sulfur-containing compounds: New {QSPR} models",
• journal = "Journal of Molecular Graphics and Modelling",
• volume = "101",
• pages = "107700",
• year = "2020",
• ISSN = "1093-3263",
• DOI = "doi:10.1016/j.jmgm.2020.107700",
• URL = "http://www.sciencedirect.com/science/article/pii/S1093326320304897",
• keywords = "genetic algorithms, genetic programming, Critical properties, Sulfur-containing compounds, Quantitative structure-property relationship (QSPR), Genetic programming (GP), The domain of applicability",
• abstract = "In this study, new models have been proposed for the prediction of different critical properties (critical temperature (TC), critical pressure (PC), critical volume (VC), and acentric factor (omega)) of the sulfur-containing compounds based on quantitative structure-property relationship (QSPR). An extensive data set containing experimental data of over 130 different sulfur-containing compounds was employed. Enhanced Replacement Method (ERM) was applied for subset variable selection. Based on ERM selected descriptors, two different models, including linear model and genetic programming (GP) based non-linear model have been proposed for each critical property. The predicted values of each target were in good agreement with the experimental data. For GP-based models, the values of the coefficient of determination (R2) were 0.936, 0.976, 0.990, and 0.917 for TC, PC, VC, and omega, respectively. After revisiting the available QSPR models, it was found that the domain of applicability of new models has been expanded",

}

Genetic Programming entries for Zahra Ghomisheh Ali Ebrahimpoor Gorji Mohammad Amin Sobati

Citations