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 r2 value of 0.964 and a cross validated coefficient r2 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.

The challenges of drug discovery are largely overcome by computer aided designing and among various drug designing techniques, ligand based drug designing proves to be an effective one. Looking at the usefulness, in the present investigation ligand-based pharmacophore models has been developed by analyzing common chemical features of phosphodiesterase IV (PDE4) inhibitors. A dataset of 38 indolizine derivatives was selected in order to built pharmacophore models which were developed by using pharmacophoric features viz. hydrogen bond acceptor (A) and aromatic ring (R).In order to build up a statistically significant model, ARRRR.30 hypothesis was selected among different developed hypothesis with a R 2 value 0.880. The selected hypothesis ARRRR.30 was further validated by performing external validation on a test set where R 2 was found to be 0.804 (between experimental and predicted activity). The developed model could be an efficient tool to develop new PDE4 inhibitors.