Modern drug research now depends on machine learning-based virtual screening to effectively find biologically active molecules from large chemical libraries. Two main strategies are used in this technique to find therapeutic candidates: (i) ligand-based drug design, which depends just on ligand information, and (ii) receptor-based drug design, which combines target and ligand data ( ) (Table . Whereas receptor-based methods use molecular docking to forecast binding affinities and interactions, ligand-based techniques examine molecular similarity and structure-activity connections. These techniques, taken together, improve the accuracy and scope of virtual screening, therefore hastening the identification of possible drug candidates with specific pharmacological effects ( ).