methods for lead generation and lead optimization in the drug discovery process are of immense importance in reducing the cycle time and cost as well as to amplify the productivity of drug discovery research. These computational methods are generally categorized as ligand-based methods and structure-based methods. In case of ligand-based methods, when biological activities of multiple hits are known, a more sophisticated class of computational techniques known as pharmacophore identification methods is often employed to deduce the essential features required for the biological activity. A pharmacophore is an abstract description of molecular features which are necessary for molecular recognition of a ligand by a biological macromolecule. Due to the advantage in efficiency in the virtual screening, the pharmacophore model method is now a potent tool in the area of drug discovery. However, the often cited drawback of the ligand-based methods is that they do not provide detailed structural information to help medicinal chemists in designing new molecules. The availability of the detailed structural information is critical especially during the lead optimization stage of the discovery process. While, structure-based pharmacophore methodology which involves generation of pharmacophore models directly from complex crystal structures is more reliable because it imposes the necessary constraints required for interaction and selectivity. Diverse inhibitor binding modes can be attained from ligand-based and structure-based pharmacophore modeling methodologies especially if many complex crystal structures are available for the target enzyme. In this view, a strategy that integrates the advantages of multiple pharmacophore modeling and molecular docking approaches has been applied for the current study in order to identify compounds that contain the important 1009820-21-6 chemical features to inhibit chymase enzyme. This strategy has been R547 chemical information successfully applied for identification of compounds from the chemical database that can strongly bind at the active site of the target and thereby act as competitive inhibitors to the chymase. Finally, four druglike compounds from the database are reported as possible inhibitors for chymase enzyme. In final phase of current study, we have carri
