One must have a representation of the drug candidate and targets if one is training a machine learning model utilising ligand and receptor (target) information ( ). Ligandbased drug design is the machine learning-based analysis of molecular characteristics and chemical fingerprints of active and inactive compounds ( ). By matching newly discovered compounds to known active molecules, ML systems can use patterns found from this data to forecast their activity. This approach is practical when there is a shortage of particular and thorough structural data about the receptor being targeted or when one wants to target several receptors with similar binding sites for ligands ( ).