PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

Detalhes bibliográficos
Autor(a) principal: Jiao,Long
Data de Publicação: 2015
Outros Autores: Wang,Xiaofei, Bing,Shan, Xue,Zhiwei, Li,Hua
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Química Nova (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422015000400510
Resumo: The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.
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spelling PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEXQSPRmolecular distance-edge vector indexPCDD/Fsboiling pointThe quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.Sociedade Brasileira de Química2015-05-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422015000400510Química Nova v.38 n.4 2015reponame:Química Nova (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.5935/0100-4042.20150025info:eu-repo/semantics/openAccessJiao,LongWang,XiaofeiBing,ShanXue,ZhiweiLi,Huaeng2015-07-31T00:00:00Zoai:scielo:S0100-40422015000400510Revistahttps://www.scielo.br/j/qn/ONGhttps://old.scielo.br/oai/scielo-oai.phpquimicanova@sbq.org.br1678-70640100-4042opendoar:2015-07-31T00:00Química Nova (Online) - Sociedade Brasileira de Química (SBQ)false
dc.title.none.fl_str_mv PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX
title PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX
spellingShingle PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX
Jiao,Long
QSPR
molecular distance-edge vector index
PCDD/Fs
boiling point
title_short PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX
title_full PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX
title_fullStr PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX
title_full_unstemmed PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX
title_sort PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX
author Jiao,Long
author_facet Jiao,Long
Wang,Xiaofei
Bing,Shan
Xue,Zhiwei
Li,Hua
author_role author
author2 Wang,Xiaofei
Bing,Shan
Xue,Zhiwei
Li,Hua
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Jiao,Long
Wang,Xiaofei
Bing,Shan
Xue,Zhiwei
Li,Hua
dc.subject.por.fl_str_mv QSPR
molecular distance-edge vector index
PCDD/Fs
boiling point
topic QSPR
molecular distance-edge vector index
PCDD/Fs
boiling point
description The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.
publishDate 2015
dc.date.none.fl_str_mv 2015-05-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422015000400510
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422015000400510
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.5935/0100-4042.20150025
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Química
publisher.none.fl_str_mv Sociedade Brasileira de Química
dc.source.none.fl_str_mv Química Nova v.38 n.4 2015
reponame:Química Nova (Online)
instname:Sociedade Brasileira de Química (SBQ)
instacron:SBQ
instname_str Sociedade Brasileira de Química (SBQ)
instacron_str SBQ
institution SBQ
reponame_str Química Nova (Online)
collection Química Nova (Online)
repository.name.fl_str_mv Química Nova (Online) - Sociedade Brasileira de Química (SBQ)
repository.mail.fl_str_mv quimicanova@sbq.org.br
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