Thrombin plays a key role in the regulation of hemostasis and thrombosis. Inhibition of thrombin is therefore an effective therapeutic target to prevent the formation of blood clots and related thromboembolism disorders. Hence, we have developed chemical feature based pharmacophore models of thrombin inhibitors. The best hypothesis, Hypo1, is characterized with two hydrogen bond acceptors (A), one hydrophobic (H) and one ring aromatic (R) feature. Hypo1 was cross validated using several techniques to prove its validity and statistical significance. The well validated model Hypo1 was used as a 3D query to perform virtual screening. The scores obtained from virtual screening were sorted by applying drug-like filters and molecular docking studies. Finally, 4 compounds were obtained as drug-like leads based on scoring functions, binding modes and molecular interactions at the active site. These 4 molecules were further optimized by adding different substitutions in their side chains. When compared to the original database hits, optimized molecules showed high scoring function, good binding modes and molecular interactions. Hence, we suggest that, upon optimization, these four database hits can act as potential virtual leads to design novel thrombin inhibitors. Also, our model could be useful to retrieve the structurally diverse compounds from various databases.
Keywords: Common feature and ligand based pharmacophore, molecular docking, thrombin, thrombosis, virtual screening.