Structure-based drug design (SBDD) is widely used in the search for possible medicinal agents. Usually referred to as ligands, the procedure involves evaluating the docking posture and calculating the degree of interaction between target proteins and small molecules ( ). Generally referring to binding affinity, the computation of interaction strength uses several scoring systems. Target protein physiological activity is directly proportional to the intensity of their interactions in relation to ligands. Therapeutic candidates thus are selected depending on their capacity to firmly attach to the target protein ( ). Accurate binding affinity prediction with the help of ML models can decrease the cost of a de novo drug design by utilising the projected binding affinity of the ligand in a library for virtual screening or lead optimisation ( ). To achieve this objective, conventional scoring functions have been suggested, such as empirical scoring functions ( ), knowledgebased ( ), and force-field-based ( ); typically, these characteristics encompass hydrogen bonds involving desolvation, van der Waals forces (view), and hydrophobic interactions. The coefficient of each descriptor is determined by considering the established binding affinity of protein-ligand complexes ( ). ML-based scoring functions have been developed to enhance the performance of conventional scoring functions.