Recently, there have been many computational methods with the explicit goal of producing novel drug-like molecules, which have been demonstrated by applying them to specific disease pathways. Most of these methods are classified as ligand-based drug design (LBDD) or structure-based drug design (SBDD) methods. LBDD considers some necessary physicochemical properties of a few known ligands and tries to predict novel molecules while preserving these properties. SBDD instead relies on the structure of the target receptor and aims to design ligands that can target specific regions of the receptor, usually with the goal of inhibition. The protein data bank (PDB) has now grown to contain an extensive collection of 3D structures of proteins, many of which are found in the human body, a) Electronic mail: arnab.mukherjee@iiserpune.ac.in allowing the use of these structures for ligand design. A common approach to SBDD is virtual screening, where a large collection of molecules are docked to the target . Docking estimates the binding strength of these molecules, and the top few are selected for further analysis. While this approach has the benefit of being able to repurpose existing drug molecules, it is limited in its exploration of chemical space by the set of molecules chosen. Often these molecules are based on libraries containing very specific scaffolds . De novo drug design instead focuses on designing molecules anew using information about the receptor's structure as a guideline. The goal is to get novel drug-like molecules with the capacity to interact with a target protein and cause a noticeable change in its activity at very low concentrations, usually through competitive inhibition with the natural ligand.