Structure-activity Relationship Studies Of Carcinogenic Activity Of Polycyclic Aromatic Hydrocarbons Using Calculated Molecular Descriptors With Principal Component Analysis And Neural Network Methods.

dc.creatorVendrame, R
dc.creatorBraga, R S
dc.creatorTakahata, Y
dc.creatorGalvão, D S
dc.date
dc.date2015-11-27T12:19:46Z
dc.date2015-11-27T12:19:46Z
dc.date.accessioned2018-03-29T00:53:53Z
dc.date.available2018-03-29T00:53:53Z
dc.identifierJournal Of Chemical Information And Computer Sciences. v. 39, n. 6, p. 1094-104
dc.identifier0095-2338
dc.identifier
dc.identifierhttp://www.ncbi.nlm.nih.gov/pubmed/10614026
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/194460
dc.identifier10614026
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1294693
dc.descriptionRecently a new methodology based on local density of state (LDOS) calculations using topological and semiempirical methods was proposed to identify the carcinogenic activity of polycyclic aromatic hydrocarbons (PAHs). In this work we perform a comparative study of this methodology with principal component analysis (PCA) and neural networks (NN). The PCA and NN results show that LDOS quantum chemical descriptors are relevant descriptors to identify the carcinogenic activity of methylated and non-methylated PAHs. Also, we show that the combination of these distinct methodologies can be an efficient and powerful tool in the structure-activity studies of PAHs compounds. We have studied 81 methylated and non-methylated PAHs, and our study shows that with the use of these methods it is possible to correctly predict the carcinogenic activity of PAHs with accuracy higher than 80%.
dc.description39
dc.description1094-104
dc.languageeng
dc.relationJournal Of Chemical Information And Computer Sciences
dc.relationJ Chem Inf Comput Sci
dc.rightsfechado
dc.rights
dc.sourcePubMed
dc.subjectCarcinogens
dc.subjectNeural Networks (computer)
dc.subjectPolycyclic Compounds
dc.subjectStructure-activity Relationship
dc.titleStructure-activity Relationship Studies Of Carcinogenic Activity Of Polycyclic Aromatic Hydrocarbons Using Calculated Molecular Descriptors With Principal Component Analysis And Neural Network Methods.
dc.typeArtículos de revistas


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