IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH

Detalhes bibliográficos
Autor(a) principal: TUKUR, SAIDU
Data de Publicação: 2019
Outros Autores: SHALLANGWA, GIDEON ADAMU, UZAIRU, ADAMU, IBRAHIM, ABDULKADIR
Tipo de documento: Artigo
Idioma: eng
Título da fonte: The Journal of Engineering and Exact Sciences
Texto Completo: https://periodicos.ufv.br/jcec/article/view/3875
Resumo: The study of the quantitative structure-activity relationship (QSAR) was used in a set of data from 43 heterocyclic and phenylic inhibitor compounds in order to establish a correlation between the inhibitory concentrations of the compounds in question and their structures. The optimization method of the density  functional theory (DFT) was used to minimize the energy of the 3D structures using the Becke functional  hybrid Exchange (B3)  parameter with the Lee, Yang, and Parr Functional Correlation (LYP), commonly called the B3LYP functional Hybrid and 6-31G* Basis Set (B3LYP/6-31G*) method, to discover their molecular Quantum descriptors. Five models of QSAR were generated with the technique of genetic function algorithm (GFA). Among the five models generated, model 1 was selected as the best model because of its statistical significance (Friedman's LOF = 0.3008, R2 = 0.9784, R2adj = 0.9739, Qcv2 = 0.9675 and R2pred = 0.7348). The meticulous model was evaluated by means of the Leave One out cross-validation (LOO-CV) approach, external validation of the compounds of the test set, Y -randomization test and applicability domain (Williams Plot). The proposed QSAR model was highly predictive and vigorous with good validation parameters. The molecular descriptors used in the model should be considered of great importance in improving the inhibitory concentrations of the herbicides and also in the conception of new herbicides with a higher concentration of inhibitor.
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spelling IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACHHerbicideQSARMultiple Linear Regression (MLR)Genetic Function Algoeithm (GFA)Applicability DomainY- Randomization.The study of the quantitative structure-activity relationship (QSAR) was used in a set of data from 43 heterocyclic and phenylic inhibitor compounds in order to establish a correlation between the inhibitory concentrations of the compounds in question and their structures. The optimization method of the density  functional theory (DFT) was used to minimize the energy of the 3D structures using the Becke functional  hybrid Exchange (B3)  parameter with the Lee, Yang, and Parr Functional Correlation (LYP), commonly called the B3LYP functional Hybrid and 6-31G* Basis Set (B3LYP/6-31G*) method, to discover their molecular Quantum descriptors. Five models of QSAR were generated with the technique of genetic function algorithm (GFA). Among the five models generated, model 1 was selected as the best model because of its statistical significance (Friedman's LOF = 0.3008, R2 = 0.9784, R2adj = 0.9739, Qcv2 = 0.9675 and R2pred = 0.7348). The meticulous model was evaluated by means of the Leave One out cross-validation (LOO-CV) approach, external validation of the compounds of the test set, Y -randomization test and applicability domain (Williams Plot). The proposed QSAR model was highly predictive and vigorous with good validation parameters. The molecular descriptors used in the model should be considered of great importance in improving the inhibitory concentrations of the herbicides and also in the conception of new herbicides with a higher concentration of inhibitor.Universidade Federal de Viçosa - UFV2019-03-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufv.br/jcec/article/view/387510.18540/jcecvl5iss1pp0049-0062The Journal of Engineering and Exact Sciences; Vol. 5 No. 1 (2019); 0049-0062The Journal of Engineering and Exact Sciences; Vol. 5 Núm. 1 (2019); 0049-0062The Journal of Engineering and Exact Sciences; v. 5 n. 1 (2019); 0049-00622527-1075reponame:The Journal of Engineering and Exact Sciencesinstname:Universidade Federal de Viçosa (UFV)instacron:UFVenghttps://periodicos.ufv.br/jcec/article/view/3875/3309TUKUR, SAIDUSHALLANGWA, GIDEON ADAMUUZAIRU, ADAMUIBRAHIM, ABDULKADIRinfo:eu-repo/semantics/openAccess2019-04-14T18:29:08Zoai:ojs.periodicos.ufv.br:article/3875Revistahttp://www.seer.ufv.br/seer/rbeq2/index.php/req2/oai2527-10752527-1075opendoar:2019-04-14T18:29:08The Journal of Engineering and Exact Sciences - Universidade Federal de Viçosa (UFV)false
dc.title.none.fl_str_mv IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
title IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
spellingShingle IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
TUKUR, SAIDU
Herbicide
QSAR
Multiple Linear Regression (MLR)
Genetic Function Algoeithm (GFA)
Applicability Domain
Y- Randomization.
title_short IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
title_full IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
title_fullStr IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
title_full_unstemmed IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
title_sort IN-SILICO STUDY FOR PREDICTING THE INHIBITION CONCENTRATION OF SOME HETEROCYCLIC AND PHENYLIC COMPOUNDS AS POTENT HERBICIDES USING THE MLR - GFA APPROACH
author TUKUR, SAIDU
author_facet TUKUR, SAIDU
SHALLANGWA, GIDEON ADAMU
UZAIRU, ADAMU
IBRAHIM, ABDULKADIR
author_role author
author2 SHALLANGWA, GIDEON ADAMU
UZAIRU, ADAMU
IBRAHIM, ABDULKADIR
author2_role author
author
author
dc.contributor.author.fl_str_mv TUKUR, SAIDU
SHALLANGWA, GIDEON ADAMU
UZAIRU, ADAMU
IBRAHIM, ABDULKADIR
dc.subject.por.fl_str_mv Herbicide
QSAR
Multiple Linear Regression (MLR)
Genetic Function Algoeithm (GFA)
Applicability Domain
Y- Randomization.
topic Herbicide
QSAR
Multiple Linear Regression (MLR)
Genetic Function Algoeithm (GFA)
Applicability Domain
Y- Randomization.
description The study of the quantitative structure-activity relationship (QSAR) was used in a set of data from 43 heterocyclic and phenylic inhibitor compounds in order to establish a correlation between the inhibitory concentrations of the compounds in question and their structures. The optimization method of the density  functional theory (DFT) was used to minimize the energy of the 3D structures using the Becke functional  hybrid Exchange (B3)  parameter with the Lee, Yang, and Parr Functional Correlation (LYP), commonly called the B3LYP functional Hybrid and 6-31G* Basis Set (B3LYP/6-31G*) method, to discover their molecular Quantum descriptors. Five models of QSAR were generated with the technique of genetic function algorithm (GFA). Among the five models generated, model 1 was selected as the best model because of its statistical significance (Friedman's LOF = 0.3008, R2 = 0.9784, R2adj = 0.9739, Qcv2 = 0.9675 and R2pred = 0.7348). The meticulous model was evaluated by means of the Leave One out cross-validation (LOO-CV) approach, external validation of the compounds of the test set, Y -randomization test and applicability domain (Williams Plot). The proposed QSAR model was highly predictive and vigorous with good validation parameters. The molecular descriptors used in the model should be considered of great importance in improving the inhibitory concentrations of the herbicides and also in the conception of new herbicides with a higher concentration of inhibitor.
publishDate 2019
dc.date.none.fl_str_mv 2019-03-08
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufv.br/jcec/article/view/3875
10.18540/jcecvl5iss1pp0049-0062
url https://periodicos.ufv.br/jcec/article/view/3875
identifier_str_mv 10.18540/jcecvl5iss1pp0049-0062
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://periodicos.ufv.br/jcec/article/view/3875/3309
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Viçosa - UFV
publisher.none.fl_str_mv Universidade Federal de Viçosa - UFV
dc.source.none.fl_str_mv The Journal of Engineering and Exact Sciences; Vol. 5 No. 1 (2019); 0049-0062
The Journal of Engineering and Exact Sciences; Vol. 5 Núm. 1 (2019); 0049-0062
The Journal of Engineering and Exact Sciences; v. 5 n. 1 (2019); 0049-0062
2527-1075
reponame:The Journal of Engineering and Exact Sciences
instname:Universidade Federal de Viçosa (UFV)
instacron:UFV
instname_str Universidade Federal de Viçosa (UFV)
instacron_str UFV
institution UFV
reponame_str The Journal of Engineering and Exact Sciences
collection The Journal of Engineering and Exact Sciences
repository.name.fl_str_mv The Journal of Engineering and Exact Sciences - Universidade Federal de Viçosa (UFV)
repository.mail.fl_str_mv
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