Recent developments include the integration of machine learning techniques with traditional scoring functions to enhance their predictive accuracy. These advanced scoring functions are trained on large datasets of known protein-ligand interactions to better generalize across different systems. While scoring functions provide a quantitative measure to evaluate and rank ligand poses, their limitations necessitate careful analysis and often the use of additional validation strategies to ensure the selection of the most biologically relevant pose .