The validity of the pharmacophore models was established by Fischer’s randomization test, internal and external test set predictions. The complementary nature of ligand and structure-based model has augmented the statistical findings of both the pharmacophores. The significance of the present study is clearly reflected by the identification of four highly potent lead compounds as protease inhibitors. All molecular modeling calculations were performed on recent software package Catalyst which has an in-build pharmacophore generation facility. Catalyst is an integrated commercially available software package that generates pharmacophores, commonly referred to as hypotheses. It enables the use of structure and activity data for a set of lead compounds to create a hypothesis, thus characterizing the activity of the lead set. HypoGen algorithm in Catalyst allows identification of AZ 960 structure hypotheses that are common to the ‘‘active’’ molecules in the training set but at the same time not present in the ‘‘inactives’’. A series of 47 compounds belonging to the cyclic cyanoguanidines and cyclic urea derivatives and their corresponding biological data represented as Ki values in nM reported by Jadhav et al. were employed for the present pharmacophore generation study in view of the following reasons: pharmacophore modeling studies have not been performed on this series, series under consideration exhibit well defined biological activities of its compounds, the compound in the series has large variation in biological activity for small change in the structure, maximum variation in the biological activity, and diversity in the structures. All the molecules under consideration were randomly split into training and test set. Training and test set were comprised of 33 and 14 compounds respectively. Energy minimization was carried using CHARMM force field. The Catalyst software reconfigure the generated structures at the minimum potential energy form using CHARMM force field. The CHARMM program in Catalyst allows generation and analysis of a wide range of molecular simulations. The Catalyst model treats the molecular structures as templates comprising chemical functions localized in space that will bind effectively with complementary functions on the respective binding proteins. The most relevant chemical features are extracted from a small set of compounds that cover a broad range of activity. Molecular flexibility is taken into account by considering each compound as an ensemble of conformers representing different accessible areas in 3D space. The conformation is of great importance for the mode of drug action since it relies on the easy accessibility of the reactive groups. Conformations for all molecules under study were generated using the ‘‘best’’ option with an energy cut-off of 20 kcal/mol. The maximum number of conformations to be generated for any molecule was set to 250. This is because Catalyst considers only the first 250 conformations in hypothesis generation. Catalyst generates random conformations to maximally span the accessible conformational space of a molecule and not necessarily only the local minima. In this light, the conformational models of the compounds will include some higher-energy structures that may be meaningful for receptor binding, since potentially favorable interactions with the latter will then compensate for the excessive conformational energy. The interaction map often displays a large number of features, especially when the receptor is capable of binding a variety of ligands and has a number of different binding modes. Thus, deriving pharmacophore models directly from the interaction map can be quite complicated. To overcome this problem, neighboring features of the same type were grouped to the same cluster. The feature closest to the geometric center of the cluster was selected to represent the cluster, whereas the rest of the features were omitted. However, even after clustering the numbers of the features were still too high to use all of them in a single query. A query composed of all the features may fail to retrieve any hits from the database/ compound library. Therefore, multiple 3D queries, composed of fewer numbers of features, were generated from the interaction map by considering all the possible combinations. The final model constructed was subjected to non feature atoms exclusion. The exclusion constraint feature is an object that represents an excluded volume in space, within a given radius. The excluded volumes were placed on regions of space that are occupied by the inactive molecules but not the active molecules.