Development of nonlinear quantitative structure-activity relationships using rbf networks and evolutionary computing

Patrinos, P; Alexandridis, A; Afantitis, A; Sarimveis, H; Igglesi-Markopoulou, O

dc.contributor.author	Patrinos, P	en
dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Afantitis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Igglesi-Markopoulou, O	en
dc.date.accessioned	2014-03-01T01:20:13Z
dc.date.available	2014-03-01T01:20:13Z
dc.date.issued	2004	en
dc.identifier.issn	15707946	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15861
dc.subject	Evolutionary Computing	en
dc.subject	Genetic Algorithms	en
dc.subject	Neural Networks	en
dc.subject	QSAR	en
dc.subject	Radial Basis Functions	en
dc.subject	Simulating Annealing	en
dc.title	Development of nonlinear quantitative structure-activity relationships using rbf networks and evolutionary computing	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S1570-7946(04)80110-X	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S1570-7946(04)80110-X	en
heal.publicationDate	2004	en
heal.abstract	Quantitative Structure Activity Relationships (QSARs) are mathematical models that correlate structural or property descriptions of compounds (hydrophobicity, topology, electronic properties etc.) with activities, such as chemical measurements and biological assays. In this paper we propose a modeling methodology suitable for QSAR studies which selects the proper descriptors based on evolutionary computing and finally produces Radial Basis Function (RBF) neural network models. The method is successfully applied to the benchmark Selwood data set. © 2004 Elsevier B.V. All rights reserved.	en
heal.journalName	Computer Aided Chemical Engineering	en
dc.identifier.doi	10.1016/S1570-7946(04)80110-X	en
dc.identifier.volume	18	en
dc.identifier.issue	C	en
dc.identifier.spage	265	en
dc.identifier.epage	270	en