Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches.
Bioorg Med Chem
; 14(1): 200-13, 2006 Jan 01.
Article
em En
| MEDLINE
| ID: mdl-16185882
Inhibition of farnesyltransferase (FT) enzyme by a set of 78 thiol and non-thiol peptidomimetic inhibitors was successfully modeled by a genetic neural network (GNN) approach, using radial distribution function descriptors. A linear model was unable to successfully fit the whole data set; however, the optimum Bayesian regularized neural network model described about 87% inhibitory activity variance with a relevant predictive power measured by q2 values of leave-one-out and leave-group-out cross-validations of about 0.7. According to their activity levels, thiol and non-thiol inhibitors were well-distributed in a topological map, built with the inputs of the optimum non-linear predictor. Furthermore, descriptors in the GNN model suggested the occurrence of a strong dependence of FT inhibition on the molecular shape and size rather than on electronegativity or polarizability characteristics of the studied compounds.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Peptídeos
/
Compostos de Sulfidrila
/
Modelos Moleculares
/
Redes Neurais de Computação
/
Mimetismo Molecular
/
Inibidores Enzimáticos
/
Farnesiltranstransferase
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Bioorg Med Chem
Assunto da revista:
BIOQUIMICA
/
QUIMICA
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Cuba
País de publicação:
Reino Unido