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Global Journal of Physical Chemistry 2012, 3: 13
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Research Article
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Free Article
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Quantitative structure–property relationship studies for predicting gas to carbon tetrachloride solvation enthalpy based on partial least squares,
artificial neural network and support vector machine
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Zahra Dashtbozorgia, Hassan Golmohammadib, William E. Acree. Jr.c
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a Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran
b Department of Chemistry, Mazandaran University, P. O. Box 453, Babolsar, Iran
c Department of Chemistry, P. O. Box 305070, University of North Texas, Denton, TX 76203-5070, USA
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Abstract |
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In the present work, partial least squares (PLS), artificial neural network (ANN) and support vector machine (SVM) techniques were used for quantitative structure–property relationship (QSPR) studies of gas to carbon tetrachloride solvation enthalpy (ΔHSolv) of various organic compounds based on molecular descriptors calculated from the optimized structures. Different kinds of molecular descriptors were calculated to characterize the molecular structures of compounds, such as constitutional, topological, charge, and geometric descriptors. The variable selection method of genetic algorithm-partial least squares (GA-PLS) was employed to select most favorable subset of descriptors. The five descriptors selected using GA-PLS were used as inputs of ANN and SVM to predict the gas to carbon tetrachloride solvation enthalpy. The correlation coefficients, R, between experimental and predicted solvation enthalpy for the test set by PLS, ANN and SVM are 0.922, 0.985 and 0.990 respectively. Satisfactory results indicated that the GA-PLS approach is a very effective method for variable selection and the predictive ability of the SVM model is superior to those obtained by PLS and ANN. The obtained results demonstrate that SVM can be used as a substitute powerful modeling tool for QSPR studies.
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Keywords |
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Gas to carbon tetrachloride solvation enthalpy; Quantitative structure–property relationship; Partial least squares; Artificial neural network; Support vector machine
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