Computational approaches to identify small molecule modulators of new drug targets

The PhD program was aimed at medicinal chemistry research, extensively focusing on computational drug design approaches to identify small molecule modulators of important drug targets. The first chapter of this thesis describes the development of fingerprint-based scoring function for improving the docking reliability of Autodock4.2 and predicting the most reliable binding mode of human Carbonic anhydrase-II (hCA-II) inhibitors. The second chapter of the PhD project reports the identification of potent and reversible Monoacylglycerol lipase (MAGL) inhibitors by (a) VS procedure based on fingerprint-driven consensus docking (b) pharmacophore-guided VS study, both studies were supported by MD simulations. The third chapter describes the discovery of a new ATP-citrate lyase (ACLY) inhibitor identified by a pharmacophore-based VS with the aid of hierarchical docking, consensus docking, MD simulations and protein-ligand binding free energy calculations. The fourth chapters of this thesis involves molecular docking-guided synthesis of Hypoxia Activated Prodrugs as DNA intercalators with a potential for fluorescence tracking. The fifth and last chapter of the thesis describes the identification of Aldehyde dehydrogenase 1A3 (ALDH1A3) inhibitors through a mixed ligand-based and receptor-based VS study, supported by consensus docking and MD simulations.