The subject of the present study is to develop QSAR models and explore the key molecular features of chymase inhibitors influencing the protein-ligand binding and interaction, by exploring the dependence of inhibitory activities upon various physiochemical properties of these compounds. In order to accomplish these tasks, an exclusive computational strategy is applied by using various QSAR model building techniques such as pharmacophore modeling, molecular docking, and Density Functional Theory (DFT) (Figure 2 ). In the first phase of calculations, a pharmacophore model (Hypo1) comprising key chemical features for the identification of novel and diverse chymase inhibitors has been generated. After validation, this pharmacophore model is used as a 3D structural search query to find new classes of compounds with similar chemical features from chemical databases. The obtained hits are scrutinized based on their estimated activity and calculated drug-like properties. Molecular docking is also performed for the evaluation of compounds for important binding site interactions and affinity. Finally, we have carried out DFT-based QSAR studies on a set of chymase inhibitors retaining structural diversity and a wide biological activity range, along with potent hits retrieved by newly developed pharmacophore model (Hypo1). The objective of this DFT study is two-fold. One purpose is to derive the QSAR model itself and the other is to scrutinize the usefulness of conceptual DFT quantities. Moreover, it also served as a validation technique for the generated pharmacophore model. Various electronic properties such as LUMO, HOMO, and locations of molecular electrostatic potentials, are computed. The results of this study are expected to explore the crucial molecular features contributing to binding specificity and be useful for understanding the molecular mechanism by which these compounds act and can be further utilized to get compounds with better activity by rational modification.