Besides dSPRINT ( ) (Figure ), several structure-based approaches have been developed to find residues within protein binding sites and solve the time-consuming and expensive nature of conventional techniques ( ). For instance, the FunFold methods use binding residues ( ) to superimpose structural templates with significant ligands onto the target model. Combining domestically developed TM-SITE and S-SITE algorithms with outside prediction tools, including COFACTOR, FINDSITE, and Con-Cavity, the COACH consensus approach combines One and achieves this integration by using a supervised learning technique ( ). P2Rank ( ) is another instance of structural-based approaches applied in binding site identification. Designed especially for predicting the sites of ligand binding to protein structures, P2Rank is a powerful computer tool. P2Rank investigates protein surface physicochemical and geometric properties using machine learning (ML) approaches. This work helps to identify likely binding locations precisely. Trained on a large dataset of known protein-ligand interactions, the ML component of P2Rank gains knowledge of patterns and features separating binding sites from non-binding regions ( ). P2Rank can generate exact forecasts even in the absence of such comparable structures. Among P2Rank's various advantages are its excellent accuracy, potency, and ability to manage several protein structures. These traits make drug discovery and development valuable. P2Rank aids in the identification of fresh binding sites, enabling the synthesis of new drugs and reusing existing compounds ( ). At last, this accelerates the whole drug development process ( ). The examples show that using ML for binding site predictions offers a good substitute for past methods' time-consuming and costly character.