Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors

Melagraki, G; Afantitis, A; Sarimveis, H; Koutentis, PA; Kollias, G; Igglessi-Markopoulou, O

dc.contributor.author	Melagraki, G	en
dc.contributor.author	Afantitis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Koutentis, PA	en
dc.contributor.author	Kollias, G	en
dc.contributor.author	Igglessi-Markopoulou, O	en
dc.date.accessioned	2014-03-01T01:31:41Z
dc.date.available	2014-03-01T01:31:41Z
dc.date.issued	2009	en
dc.identifier.issn	1381-1991	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19888
dc.subject	HDAC	en
dc.subject	Histone deacetylases	en
dc.subject	Hydroxamic acids	en
dc.subject	In silico screening	en
dc.subject	QSAR	en
dc.subject.classification	Biochemistry & Molecular Biology	en
dc.subject.classification	Chemistry, Applied	en
dc.subject.classification	Chemistry, Medicinal	en
dc.subject.classification	Chemistry, Multidisciplinary	en
dc.subject.other	histone deacetylase inhibitor	en
dc.subject.other	article	en
dc.subject.other	drug activity	en
dc.subject.other	drug inhibition	en
dc.subject.other	drug screening	en
dc.subject.other	drug structure	en
dc.subject.other	IC 50	en
dc.subject.other	multiple linear regression analysis	en
dc.subject.other	physical chemistry	en
dc.subject.other	priority journal	en
dc.subject.other	quantitative structure activity relation	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer Simulation	en
dc.subject.other	Drug Discovery	en
dc.subject.other	Enzyme Inhibitors	en
dc.subject.other	Histone Deacetylase Inhibitors	en
dc.subject.other	Hydroxamic Acids	en
dc.subject.other	Models, Chemical	en
dc.subject.other	Physicochemical Phenomena	en
dc.subject.other	Pyridines	en
dc.subject.other	Quantitative Structure-Activity Relationship	en
dc.subject.other	Regression Analysis	en
dc.subject.other	Reproducibility of Results	en
dc.subject.other	Thiophenes	en
dc.title	Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s11030-009-9115-2	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s11030-009-9115-2	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	A linear Quantitative Structure-Activity Relationship (QSAR) is developed in this work for modeling and predicting HDAC inhibition by 5-pyridin-2-yl- thiophene-2-hydroxamic acids. In particular, a five-variable model is produced by using the Multiple Linear Regression (MLR) technique and the Elimination Selection-Stepwise Regression Method (ES-SWR) on a database that consists of 58 recently discovered 5-pyridin-2-yl-thiophene-2-hydroxamic acids and 69 descriptors. The physical meaning of the selected descriptors is discussed in detail. The validity of the proposed MLR model is established using the following techniques: cross validation, validation through an external test set and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. Based on the produced model, an in silico-screening study explores novel structural patterns and suggests new potent lead compounds. © 2009 Springer Science+Business Media B.V.	en
heal.publisher	SPRINGER	en
heal.journalName	Molecular Diversity	en
dc.identifier.doi	10.1007/s11030-009-9115-2	en
dc.identifier.isi	ISI:000268304500005	en
dc.identifier.volume	13	en
dc.identifier.issue	3	en
dc.identifier.spage	301	en
dc.identifier.epage	311	en