All the 20 training set compounds associated with their conformations were submitted to the HypoGen module of DS. The HypoGen algorithm implemented for the pharmacophore hypothesis generation process is executed in three phases, namely, constructive, subtractive, and optimization phases. In constructive phase, identification of features common to the most active compounds takes place whereas all pharmacophoric features that are also present in the least active compounds are removed in subtractive phase. Finally, in the optimization phase, the hypothesis score is improved by regression parameters which are used for the estimation of the activity value of each training set compound. The relationship between the geometric fit value and activity value is utilized for this computation. Pharmacophore hypotheses showing best correlation in the 3D arrangement of features in a given training set compounds with the corresponding pharmacological activities are formed and ranked. Several structure activity relationship (SAR) pharmacophore models were derived from training set compounds using HypoGen module of DS. In this study, the top 10 hypotheses which were returned by the hypotheses generation process with significant statistical parameters were selected for further calculations.