THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista de Engenharia Química e Química |
Texto Completo: | https://periodicos.ufv.br/jcec/article/view/2509 |
Resumo: | A Quantitative Structure Activity Relationship (QSAR) study has been attempted on ciprofloxacin derivatives as potent anti-lung cancer. QSAR models were derived with the aid of multi-linear regression (MLR) approach using topological, molecular shape, electronic and structural descriptors. The predictive ability of the QSAR models generated were validated and the best model selected has squared correlation coefficient (R2) of 0.954801, adjusted squared correlation coefficient (Radj) of 0.939265, Leave one out (LOO) cross validation coefficient () value of 0.907523. The external validation set used for confirming the predictive power of the model has its R2pred of 0.8387. The QSAR models point out that AATSC2m, VR3_Dzp and BIC2 are the important descriptors effectively describing the bioactivity of these compounds. |
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Revista de Engenharia Química e Química |
repository_id_str |
|
spelling |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACHCiprofloxacinDescriptorGenetic Function ApproximationLung CancerQSAR.A Quantitative Structure Activity Relationship (QSAR) study has been attempted on ciprofloxacin derivatives as potent anti-lung cancer. QSAR models were derived with the aid of multi-linear regression (MLR) approach using topological, molecular shape, electronic and structural descriptors. The predictive ability of the QSAR models generated were validated and the best model selected has squared correlation coefficient (R2) of 0.954801, adjusted squared correlation coefficient (Radj) of 0.939265, Leave one out (LOO) cross validation coefficient () value of 0.907523. The external validation set used for confirming the predictive power of the model has its R2pred of 0.8387. The QSAR models point out that AATSC2m, VR3_Dzp and BIC2 are the important descriptors effectively describing the bioactivity of these compounds.Universidade Federal de Viçosa - UFV2019-03-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufv.br/jcec/article/view/250910.18540/jcecvl5iss1pp0125-0136The Journal of Engineering and Exact Sciences; Vol. 5 No. 1 (2019); 0125-0136The Journal of Engineering and Exact Sciences; Vol. 5 Núm. 1 (2019); 0125-0136The Journal of Engineering and Exact Sciences; v. 5 n. 1 (2019); 0125-01362527-1075reponame:Revista de Engenharia Química e Químicainstname:Universidade Federal de Viçosa (UFV)instacron:UFVenghttps://periodicos.ufv.br/jcec/article/view/2509/3259Adeniji, Shola ElijahOvaku, Momohjimoh IdrisSaidu, TukurUgochukwu, Ahanonu SaviourShallangwa, GideonUzairu, Adamuinfo:eu-repo/semantics/openAccess2019-04-12T14:38:21Zoai:ojs.periodicos.ufv.br:article/2509Revistahttp://www.seer.ufv.br/seer/rbeq2/index.php/req2/indexONGhttps://periodicos.ufv.br/jcec/oaijcec.journal@ufv.br||req2@ufv.br2446-94162446-9416opendoar:2019-04-12T14:38:21Revista de Engenharia Química e Química - Universidade Federal de Viçosa (UFV)false |
dc.title.none.fl_str_mv |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
title |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
spellingShingle |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH Adeniji, Shola Elijah Ciprofloxacin Descriptor Genetic Function Approximation Lung Cancer QSAR. |
title_short |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
title_full |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
title_fullStr |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
title_full_unstemmed |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
title_sort |
THEORETICAL MODELLING FOR INVESTIGATING SOME ACTIVE COMPOUNDS AS POTENT INHIBITORS AGAINST LUNG CANCER: A MULTI-LINEAR REGRESSION APPROACH |
author |
Adeniji, Shola Elijah |
author_facet |
Adeniji, Shola Elijah Ovaku, Momohjimoh Idris Saidu, Tukur Ugochukwu, Ahanonu Saviour Shallangwa, Gideon Uzairu, Adamu |
author_role |
author |
author2 |
Ovaku, Momohjimoh Idris Saidu, Tukur Ugochukwu, Ahanonu Saviour Shallangwa, Gideon Uzairu, Adamu |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Adeniji, Shola Elijah Ovaku, Momohjimoh Idris Saidu, Tukur Ugochukwu, Ahanonu Saviour Shallangwa, Gideon Uzairu, Adamu |
dc.subject.por.fl_str_mv |
Ciprofloxacin Descriptor Genetic Function Approximation Lung Cancer QSAR. |
topic |
Ciprofloxacin Descriptor Genetic Function Approximation Lung Cancer QSAR. |
description |
A Quantitative Structure Activity Relationship (QSAR) study has been attempted on ciprofloxacin derivatives as potent anti-lung cancer. QSAR models were derived with the aid of multi-linear regression (MLR) approach using topological, molecular shape, electronic and structural descriptors. The predictive ability of the QSAR models generated were validated and the best model selected has squared correlation coefficient (R2) of 0.954801, adjusted squared correlation coefficient (Radj) of 0.939265, Leave one out (LOO) cross validation coefficient () value of 0.907523. The external validation set used for confirming the predictive power of the model has its R2pred of 0.8387. The QSAR models point out that AATSC2m, VR3_Dzp and BIC2 are the important descriptors effectively describing the bioactivity of these compounds. |
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/2509 10.18540/jcecvl5iss1pp0125-0136 |
url |
https://periodicos.ufv.br/jcec/article/view/2509 |
identifier_str_mv |
10.18540/jcecvl5iss1pp0125-0136 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.ufv.br/jcec/article/view/2509/3259 |
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); 0125-0136 The Journal of Engineering and Exact Sciences; Vol. 5 Núm. 1 (2019); 0125-0136 The Journal of Engineering and Exact Sciences; v. 5 n. 1 (2019); 0125-0136 2527-1075 reponame:Revista de Engenharia Química e Química instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
UFV |
institution |
UFV |
reponame_str |
Revista de Engenharia Química e Química |
collection |
Revista de Engenharia Química e Química |
repository.name.fl_str_mv |
Revista de Engenharia Química e Química - Universidade Federal de Viçosa (UFV) |
repository.mail.fl_str_mv |
jcec.journal@ufv.br||req2@ufv.br |
_version_ |
1800211188236156928 |