Prediction of air to liver partition coefficient for volatile organic compounds using QSAR approaches

Journal of Molecular Liquids

et al. 2012

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

QSPR studies for predicting gas to acetone and gas to acetonitrile solvation enthalpies using support vector machine

Toubaei

Journal of Molecular Liquids

et al. 2012

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“…They have the aptitude to capture the relationship between input and an output variable from given outlines and this facilitates their ability to solve large-scale complex problems. A detailed description of the theory behind a neural network has been adequately described in our previous publications [37][38][39][40][41].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of QSPR for the prediction of gas to 1-octanol solvation enthalpy using support vector regression

Physics and Chemistry of Liquids

Acree

2013

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A quantitative structure property relationship model was developed to predict gas to 1-octanol solvation enthalpy (DH Solv ) of 127 different organic compounds using support vector machine (SVM). The variable selection method of genetic algorithm (GA) was employed to select optimal subset of descriptors. The five descriptors selected by GA were used as inputs for construction of the multiple linear regression (MLR), artificial neural network (ANN) and SVM models. The standard errors of for the prediction data set given by MLR, ANN and SVM were 6.438, 4.004 and 2.711 kJ mol À1 , respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and ANN models. Furthermore, the derived models also provided some insight into what structural features are related to the gas to DH Solv of various organic compounds.

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“…These networks learn from the training data by changing the connection weights [42]. A detailed explanation of the theory behind a neural network has been adequately described in our previous works [43][44][45][46][47].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Support vector regression based QSPR for the prediction of retention time of pesticide residues in gas chromatography–mass spectroscopy

Konoz

2013

Microchemical Journal

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