APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES

The toxicity of some phenol derivatives over tetrahymena pyriformis were modeled by using multivariate image analysis (MIA) descriptors applied to quantitative structure-toxicity relationship (QSTR) method. MIA descriptors are derived from pixels of two-dimensional (2D) chemical structures that may...

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Autores principales:	GARKANI-NEJAD,ZAHRA, SALEHFARD,FATHIYEH
Lenguaje:	English
Publicado:	Sociedad Chilena de Química 2013
Materias:	Multivariate image analysis (MIA) Quantitative structure-toxicity relationship (QSTR) Principal component-artificial neural network (PC-ANN) phenol derivatives
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072013000400070
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spelling	oai:scielo:S0717-970720130004000702014-09-02APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVESGARKANI-NEJAD,ZAHRASALEHFARD,FATHIYEH Multivariate image analysis (MIA) Quantitative structure-toxicity relationship (QSTR) Principal component-artificial neural network (PC-ANN) phenol derivatives The toxicity of some phenol derivatives over tetrahymena pyriformis were modeled by using multivariate image analysis (MIA) descriptors applied to quantitative structure-toxicity relationship (QSTR) method. MIA descriptors are derived from pixels of two-dimensional (2D) chemical structures that may be built by using any appropriate drawing software. QSTR analysis has been done by using partial least square regression (PLS), correlation ranking-principal component regression (CR-PCR) and correlation ranking-principal component-artificial neural network (CR-PC-ANN) modeling methods. For the ANN models two different weight update functions of Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) were investigated. Finally, obtained results using different linear and nonlinear modeling methods were compared, showing that PLS and CR-PC-ANN-SCG methods present high performance for prediction of the toxicity of phenol derivatives. Also, these results indicated that MIA descriptors may be useful to predict toxicity of phenol derivatives. Squared correlation coefficient (R²) and standard error using the PLS method for the training set were 0.996 and 0.042 and for the test set were 0.996 and 0.048, respectively. Squared correlation coefficient (R²) and standard error using the CR-PC-ANN-SCG method were respectively, 0.999 and 0.002 for the training set, 0.999 and 0.001 for the test set and 0.999 and 0.002 for the validation set.info:eu-repo/semantics/openAccessSociedad Chilena de QuímicaJournal of the Chilean Chemical Society v.58 n.4 20132013-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072013000400070en10.4067/S0717-97072013000400070
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Multivariate image analysis (MIA) Quantitative structure-toxicity relationship (QSTR) Principal component-artificial neural network (PC-ANN) phenol derivatives
spellingShingle	Multivariate image analysis (MIA) Quantitative structure-toxicity relationship (QSTR) Principal component-artificial neural network (PC-ANN) phenol derivatives GARKANI-NEJAD,ZAHRA SALEHFARD,FATHIYEH APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES
description	The toxicity of some phenol derivatives over tetrahymena pyriformis were modeled by using multivariate image analysis (MIA) descriptors applied to quantitative structure-toxicity relationship (QSTR) method. MIA descriptors are derived from pixels of two-dimensional (2D) chemical structures that may be built by using any appropriate drawing software. QSTR analysis has been done by using partial least square regression (PLS), correlation ranking-principal component regression (CR-PCR) and correlation ranking-principal component-artificial neural network (CR-PC-ANN) modeling methods. For the ANN models two different weight update functions of Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) were investigated. Finally, obtained results using different linear and nonlinear modeling methods were compared, showing that PLS and CR-PC-ANN-SCG methods present high performance for prediction of the toxicity of phenol derivatives. Also, these results indicated that MIA descriptors may be useful to predict toxicity of phenol derivatives. Squared correlation coefficient (R²) and standard error using the PLS method for the training set were 0.996 and 0.042 and for the test set were 0.996 and 0.048, respectively. Squared correlation coefficient (R²) and standard error using the CR-PC-ANN-SCG method were respectively, 0.999 and 0.002 for the training set, 0.999 and 0.001 for the test set and 0.999 and 0.002 for the validation set.
author	GARKANI-NEJAD,ZAHRA SALEHFARD,FATHIYEH
author_facet	GARKANI-NEJAD,ZAHRA SALEHFARD,FATHIYEH
author_sort	GARKANI-NEJAD,ZAHRA
title	APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES
title_short	APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES
title_full	APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES
title_fullStr	APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES
title_full_unstemmed	APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES
title_sort	application of multivariate image analysis - quantitative structure toxicity relationship study for modeling the toxicity of phenol derivatives
publisher	Sociedad Chilena de Química
publishDate	2013
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072013000400070
work_keys_str_mv	AT garkaninejadzahra applicationofmultivariateimageanalysisquantitativestructuretoxicityrelationshipstudyformodelingthetoxicityofphenolderivatives AT salehfardfathiyeh applicationofmultivariateimageanalysisquantitativestructuretoxicityrelationshipstudyformodelingthetoxicityofphenolderivatives
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APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES

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