Cargando…

3D-QSAR CoMFA study of some Heteroarylpyrroles as Possible Anticandida Agents

A three dimensional quantitative structure-activity relationship study using the comparative molecular field analysis method was performed on a series of 3-aryl-4-[α-(1H-imidazol-1-yl) aryl methyl] pyrroles for their anticandida activity. This study was performed using 40 compounds, for which compar...

Descripción completa

Detalles Bibliográficos
Autores principales: Sharma, P. C., Sharma, S. V., Sharma, Archana, Suresh, B.
Formato: Texto
Lenguaje:English
Publicado: Medknow Publications 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2792495/
https://www.ncbi.nlm.nih.gov/pubmed/20046704
http://dx.doi.org/10.4103/0250-474X.41447
Descripción
Sumario:A three dimensional quantitative structure-activity relationship study using the comparative molecular field analysis method was performed on a series of 3-aryl-4-[α-(1H-imidazol-1-yl) aryl methyl] pyrroles for their anticandida activity. This study was performed using 40 compounds, for which comparative molecular field analysis models were developed using a training set of 33 compounds. Database alignment of all 33 compounds was carried out by root-mean-square fit of atoms and field fit of the steric and electrostatic molecular fields. The resulting database was analyzed by partial least squares analysis with cross-validation; leave one out and no validation to extract optimum number of components. The analysis was then repeated with bootstrapping to generate the quantitative structure-activity relationship models. The predictive ability of comparative molecular field analysis model was evaluated by using a test set of 7 compounds. The 3D- quantitative structure-activity relationship model demonstrated a good fit, having r(2) value of 0.964 and a cross validated coefficient r(2) value as 0.598. Further comparison of the coefficient contour maps with the steric and electrostatic properties of the receptor has shown a high level of compatibility and good predictive capability.