Ligand - Based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks

Afantitis, A; Melagraki, G; Koutentis, PA; Sarimveis, H; Kollias, G

dc.contributor.author	Afantitis, A	en
dc.contributor.author	Melagraki, G	en
dc.contributor.author	Koutentis, PA	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Kollias, G	en
dc.date.accessioned	2014-03-01T01:35:56Z
dc.date.available	2014-03-01T01:35:56Z
dc.date.issued	2011	en
dc.identifier.issn	0223-5234	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21253
dc.subject	β-Amyloid inhibitors	en
dc.subject	Alzheimer's disease	en
dc.subject	CP-ANN	en
dc.subject	In silico virtual screening	en
dc.subject	Kohonen map	en
dc.subject	QSAR	en
dc.subject.classification	Chemistry, Medicinal	en
dc.subject.other	amyloid beta protein	en
dc.subject.other	efenamic acid	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	biological activity	en
dc.subject.other	computer model	en
dc.subject.other	drug identification	en
dc.subject.other	drug screening	en
dc.subject.other	pharmacophore	en
dc.subject.other	prediction	en
dc.subject.other	protein aggregation	en
dc.subject.other	quantitative structure activity relation	en
dc.subject.other	Amyloid beta-Peptides	en
dc.subject.other	Cluster Analysis	en
dc.subject.other	Fenamates	en
dc.subject.other	High-Throughput Screening Assays	en
dc.subject.other	Ligands	en
dc.subject.other	Models, Molecular	en
dc.subject.other	Molecular Structure	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Predictive Value of Tests	en
dc.subject.other	Stereoisomerism	en
dc.subject.other	Structure-Activity Relationship	en
dc.title	Ligand - Based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.ejmech.2010.11.029	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.ejmech.2010.11.029	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	In this work we have developed an in silico model to predict the inhibition of beta-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic acids using Kohonen maps and Counterpropagation Artificial Neural Networks. The effects of various structural modifications on biological activity are investigated and novel structures are designed using the developed in silico model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence. (C) 2010 Elsevier Masson SAS. All rights reserved.	en
heal.publisher	ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER	en
heal.journalName	European Journal of Medicinal Chemistry	en
dc.identifier.doi	10.1016/j.ejmech.2010.11.029	en
dc.identifier.isi	ISI:000287617500005	en
dc.identifier.volume	46	en
dc.identifier.issue	2	en
dc.identifier.spage	497	en
dc.identifier.epage	508	en