Structure-activity relationship (SAR) of substituted 17 alpha-acetoxyprogesterones studied with principal component analysis and neural networks using calculated physicochemical parameters

dc.creatorVendrame, R
dc.creatorTakahata, Y
dc.date1999
dc.dateOCT 8
dc.date2014-12-02T16:30:53Z
dc.date2015-11-26T16:45:16Z
dc.date2014-12-02T16:30:53Z
dc.date2015-11-26T16:45:16Z
dc.date.accessioned2018-03-28T23:30:51Z
dc.date.available2018-03-28T23:30:51Z
dc.identifierJournal Of Molecular Structure-theochem. Elsevier Science Bv, v. 489, n. 1, n. 55, n. 66, 1999.
dc.identifier0166-1280
dc.identifierWOS:000082893600008
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/75403
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/75403
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/75403
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1274167
dc.descriptionIt was shown that the two different methods, the principal component analysis (PCA) and the neural network (NN), can classify oral progestational activity of substituted 17 alpha-acetoxyprogesterones into two categories, high active and low active, using only calculated molecular properties. The two methods can predict the category of each molecule with a fairly high percentage of success. The NN work was slightly better than the PCA in the prediction of the category. Ionization potential, molecular hardness, net atomic charges, frontier indices were found to be useful parameters for the classification of the compounds. (C) 1999 Elsevier Science B.V. All rights reserved.
dc.description489
dc.description1
dc.description55
dc.description66
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationJournal Of Molecular Structure-theochem
dc.relationTheochem-J. Mol. Struct.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectstructure-activity relationship
dc.subjectprogestational activity
dc.subject17 alpha-acetoxyprogesterones
dc.subjectprincipal component analysis
dc.subjectneural network
dc.titleStructure-activity relationship (SAR) of substituted 17 alpha-acetoxyprogesterones studied with principal component analysis and neural networks using calculated physicochemical parameters
dc.typeArtículos de revistas


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