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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Article on 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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13 p.

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Dashtbozorgi, Zahra; Golmohammadi, Hassan & Acree, William E. (William Eugene) 2012.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Arts and Sciences to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 629 times , with 6 in the last month . More information about this article can be viewed below.

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  • Cognizure
    Place of Publication: [Tamil Nadu, India]

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Article on 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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13 p.

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Abstract: 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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  • Global Journal of Physical Chemistry, 2012, Tamil Nadu: Cognizure

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  • Publication Title: Global Journal of Physical Chemistry
  • Volume: 3
  • Issue: 13
  • Peer Reviewed: Yes

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  • 2012

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  • June 28, 2013, 2:11 p.m.

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  • Feb. 24, 2014, 3:04 p.m.

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Dashtbozorgi, Zahra; Golmohammadi, Hassan & Acree, William E. (William Eugene). 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, article, 2012; [Tamil Nadu, India]. (digital.library.unt.edu/ark:/67531/metadc172358/: accessed November 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.