2.3. Validation of pharmacophore model
The pharmacophore model was validated using the test sets to investigate the capacity of the generated models. For validating pharmacophore models. The test consisted of both active and inactive compounds for PPARα (28 compounds), PPARγ (29 compounds), and PPARδ (25 compounds). The best pharmacophore model was selected from the 10 generated hypotheses based on high fit value, cost analysis, high correlation, and lowest root mean square deviation (RMSD) using the Catalyst/HypoGen module of DS. The fit value shows the quality of the mapping of compounds to the hypothesis. The cost difference is the difference between the total cost and the null cost. The overall cost of a good hypothesis is near to fixed cost and far from the null cost. The similarity and closeness in the data set with each other are measured by the correlation coefficient. The highest the correlation coefficient, the highest the similarity, and more closeness in the data set. The lower the RMSD value, the better superimposition of structures over pharmacophore models. By applying the above-mentioned statistical parameters, the best model of pharmacophore was selected. To evaluate the pharmacophore model’s prediction power, all compounds in the test set were mapped to the hypothesis model using the Ligand Pharmacophore Mapping protocol in DS.