The binding site refers to a specific localised location on a protein or biomolecule that helps other molecules, including ligands, connect and interact. Since binding sites define the drug's efficacy, strength, and specificity in controlling biological processes, the interactions between a drug molecule and its target binding site play a crucial part in deciding the medicine's mechanism of action ( ). The most common approach to the identification of a binding site is the usage of structural information, such as dSPRINT ( ) (Figure ) Undergraduate student Anat Etzion-Fuchs from Mona Singh's lab at Princeton University leads the dSPRINT project, which forecasts the sites where ligands connect to protein domains using a range of traits, including amino acid patterns and physiochemical properties. Even for proteins without crystal structures, dSPRINT precisely forecasts interactions between domains and ligands by combining machine learning classifiers with a stacking approach. Cross-valuation validates the model's performance and reveals its capacity to forecast known interaction sites precisely. Furthermore, the forecasts of dSPRINT provide fresh insights into the interactions between proteins and ligands and expose new roles for proteins whose study has not been much investigated. This helps us to understand cellular processes and project protein functions better. One may easily review and investigate the dSPRINT architecture and its predictions ( ).