2.2. Generation of Pharmacophore Model

In this study, the 3D QSAR Pharmacophore Generation module of DS was used to generate ligand-based pharmacophoric hypotheses. In brief, the possible pharmacophoric features of the training set compounds were identified with Feature Mapping protocol of DS. The training set was used for hypotheses generation through the HypoGen algorithm of 3D QSAR Pharmacophore Generation module, which was implanted in DS. During the hypotheses generation, the minimum and maximum numbers of features were set to 0 and 5, respectively. BEST algorithm of DS was employed to generate low energy conformations of the training set compounds. Uncertainty value was set to 3 while other parameters had default values. Hypotheses were generated with their resultant statistical parameters such as cost values (fixed cost and null cost), correlation (R²), root mean square deviation (RMSD), and fit values. Cost values were analyzed as per Debnath’s method [26].