A novel non-stochastic quadratic fingerprints-based approach for the “in silico” discovery of new antitrypanosomal compounds
Fecha
2005
Autores
Montero Torres, Alina
Vega, María Celeste
Marrero Ponce, Yovani
Rolóm, Miriam
Gómez Barrio, Alicia
Escario, José Antonio
Arán, Vicente J.
Martinez Fernández, Antonio R.
Meneses Marcel, Alfredo
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Resumen
A Non-Stochastic Quadratic Fingerprints-based approach is introduced to classify and design,
in a rational way, new antitrypanosomal compounds. A data set of 153 organic-chemicals; 62
with antitrypanosomal activity and 91 having other clinical uses, was processed by a k-means
cluster analysis in order to design training and predicting data sets. Afterwards, a linear
classification function was derived allowing the discrimination between active and inactive
compounds. The model classifies correctly more than 93% of chemicals in both training and
external prediction groups. The predictability of this discriminant function was also assessed
by a leave-group-out experiment, in which 10% of the compounds were removed at random at
each time and their activity a posteriori predicted. Also a comparison with models generated
using four well-known families of 2D molecular descriptors was carried out. As an
experiment of virtual lead generation, the present TOMOCOMD approach was finally
satisfactorily applied on the virtual evaluation of ten already synthesized compounds. The in
vitro antitrypanosomal activity of this series against epimastigotes forms of T. cruzi was
assayed. The model was able to predict correctly the behaviour of these compounds in 90% of
the cases.
Descripción
Palabras clave
Antitrypanosomal compounds, Chagas’ disease, Enfermedad de Chagas, Non-stochastic quadratic indices, Diseño de fármacos