Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches.
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.
González
Maykel Pérez
MP
Unit of Service, Drug Design Department, Experimental Sugar Cane Station Villa Clara-Cienfuegos, Ranchuelo, Villa Clara, C.P. 53100, Cuba.
Caballero
Julio
J
Tundidor-Camba
Alain
A
Helguera
Aliuska Morales
AM
Fernández
Michael
M
D004791:Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D051231:Farnesyltranstransferase / Q000037:antagonists & inhibitors*; D008958:Models, Molecular; D018716:Molecular Mimicry; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry / Q000494:pharmacology*; D013438:Sulfhydryl Compounds / Q000737:chemistry / Q000494:pharmacology*
D004791:Enzyme Inhibitors; D010455:Peptides; D013438:Sulfhydryl Compounds; D051231:Farnesyltranstransferase