Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines

Ниази, Али; Jameh‐Bozorghi, Saeed; Nori‐Shargh, Davood

doi:10.1016/j.jhazmat.2007.06.030

Cited by 61 publications

(24 citation statements)

References 36 publications

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“…Theory of LS-SVM has also been described clearly by Suykens and Vandewalle 11 and application of LS-SVM in quantification 15,16 and QSAR 17 reported by some of the workers. So, we will only briefly describe the theory of LS-SVM.…”

Section: Theorymentioning

confidence: 86%

Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-square Support Vector Machine

Hamzehali¹,

Ниази²

2013

Orientjchem.

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A quantitative structure-property relationship (QSPR) study is suggested for the prediction of mobilities (m) of benzoaromatic carboxylates. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Also, Dragon software was used to calculate some descriptors such as WIHM and GETAWAY. Modeling of the mobility of benzoaromatic carboxylate derivatives as a function of molecular structures was established by means of the least squares support vector machines (LS-SVM). This model was applied for the prediction of the mobility of benzoaromatic carboxylates, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction (RMSEP) of 3.734, 1.931 and 0.018 for MLR, PLS and LS-SVM, respectively. Results have shown that the introduction of LS-SVM for quantum chemical, WIHM and GETAWAY descriptors drastically enhances the ability of prediction in QSAR studies superior to multiple linear regression (MLR) and partial least squares (PLS).

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Section: Theorymentioning

confidence: 86%

Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-square Support Vector Machine

Hamzehali¹,

Ниази²

2013

Orientjchem.

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“…In this study, many algorithms available in WEKA were initially trained by using WEKA's default settings. After analysis of preliminary results, the algorithms that turned out to perform at the best (LibSVM [24][25][26][27][28], MultiLayerPerceptron [29] and SMOregression [30,31]) were re-trained by more finely tuning the parameter values in order to maximize the algorithm performance.…”

Section: Weka Computationsmentioning

confidence: 99%

“…Further investigations were performed by changing the values of parameters, which enabled tuning the process of model development. The MultiLayerPerceptron [29], SMOregression [30,31] and LibSVM [24][25][26][27][28] methods, that had shown to give better results, were exploited in a more sophisticated search.…”

Section: Tr Set Statistics Ts Set Statisticsmentioning

confidence: 99%

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

Massarelli¹,

Imbriani²,

Coi³

et al. 2009

European Journal of Medicinal Chemistry

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“…Quantitative structure-activity/property relationship (QSAR/QSPR) studies, as one of the most important areas in chemometrics, give information that is useful for molecular design and medicinal chemistry [2][3][4][5] . QSAR/QSPR models are mathematical equations relating chemical structure to a wide variety of physical, chemical, biological and technological properties.…”

Section: Introductionmentioning

confidence: 99%

APPLICATION OF MULTIVARIATE IMAGE ANALYSIS IN QSPR STUDY OF pKa OF VARIOUS ACIDS BY PRINCIPAL COMPONENTS-LEAST SQUARES SUPPORT VECTOR MACHINE

Veyseh

Hamzehali

Ниази

et al. 2015

J. Chil. Chem. Soc.

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A new implemented quantitative structure-property relationships (QSPR) method, whose descriptors achieved from bidimensional images, was suggested for the predicting of acidity constant (pK a ) of various acid. The resulted descriptors were subjected to principal component analysis (PCA) and the most significant principal components (PCs) were extracted. Multivariate image analysis applied to QSPR modeling was done by means of principal component-least squares support vector machine (PC-LSSVM) methods. The resulted model showed high prediction ability with root mean square error of prediction of 0.0195 for PC-LSSVM.

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Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines

Cited by 61 publications

References 36 publications

Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-square Support Vector Machine

Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-square Support Vector Machine

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

APPLICATION OF MULTIVARIATE IMAGE ANALYSIS IN QSPR STUDY OF pKa OF VARIOUS ACIDS BY PRINCIPAL COMPONENTS-LEAST SQUARES SUPPORT VECTOR MACHINE

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