Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.

The epidermal growth factor receptor (EGFR) protein tyrosine kinase (PTK) is an important protein target for anti-tumor drug discovery. To identify potential EGFR inhibitors, we conducted a quantitative structure-activity relationship (QSAR) study on the inhibitory activity of a series of quinazolin...

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Autores principales:	Hongying Du, Zhide Hu, Andrea Bazzoli, Yang Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2325bfb67462429095bc9e7c32132b3f
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spelling	oai:doaj.org-article:2325bfb67462429095bc9e7c32132b3f2021-11-18T06:49:48ZPrediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.1932-620310.1371/journal.pone.0022367https://doaj.org/article/2325bfb67462429095bc9e7c32132b3f2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21811593/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The epidermal growth factor receptor (EGFR) protein tyrosine kinase (PTK) is an important protein target for anti-tumor drug discovery. To identify potential EGFR inhibitors, we conducted a quantitative structure-activity relationship (QSAR) study on the inhibitory activity of a series of quinazoline derivatives against EGFR tyrosine kinase. Two 2D-QSAR models were developed based on the best multi-linear regression (BMLR) and grid-search assisted projection pursuit regression (GS-PPR) methods. The results demonstrate that the inhibitory activity of quinazoline derivatives is strongly correlated with their polarizability, activation energy, mass distribution, connectivity, and branching information. Although the present investigation focused on EGFR, the approach provides a general avenue in the structure-based drug development of different protein receptor inhibitors.Hongying DuZhide HuAndrea BazzoliYang ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 7, p e22367 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hongying Du Zhide Hu Andrea Bazzoli Yang Zhang Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.
description	The epidermal growth factor receptor (EGFR) protein tyrosine kinase (PTK) is an important protein target for anti-tumor drug discovery. To identify potential EGFR inhibitors, we conducted a quantitative structure-activity relationship (QSAR) study on the inhibitory activity of a series of quinazoline derivatives against EGFR tyrosine kinase. Two 2D-QSAR models were developed based on the best multi-linear regression (BMLR) and grid-search assisted projection pursuit regression (GS-PPR) methods. The results demonstrate that the inhibitory activity of quinazoline derivatives is strongly correlated with their polarizability, activation energy, mass distribution, connectivity, and branching information. Although the present investigation focused on EGFR, the approach provides a general avenue in the structure-based drug development of different protein receptor inhibitors.
format	article
author	Hongying Du Zhide Hu Andrea Bazzoli Yang Zhang
author_facet	Hongying Du Zhide Hu Andrea Bazzoli Yang Zhang
author_sort	Hongying Du
title	Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.
title_short	Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.
title_full	Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.
title_fullStr	Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.
title_full_unstemmed	Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.
title_sort	prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/2325bfb67462429095bc9e7c32132b3f
work_keys_str_mv	AT hongyingdu predictionofinhibitoryactivityofepidermalgrowthfactorreceptorinhibitorsusinggridsearchprojectionpursuitregressionmethod AT zhidehu predictionofinhibitoryactivityofepidermalgrowthfactorreceptorinhibitorsusinggridsearchprojectionpursuitregressionmethod AT andreabazzoli predictionofinhibitoryactivityofepidermalgrowthfactorreceptorinhibitorsusinggridsearchprojectionpursuitregressionmethod AT yangzhang predictionofinhibitoryactivityofepidermalgrowthfactorreceptorinhibitorsusinggridsearchprojectionpursuitregressionmethod
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Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method.

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