Several prominent docking programs were developed early in this field. DOCK, the original automated docking program, introduced the concept of matching ligand spheres to receptor binding sites and used grid-based scoring functions . Glide, a commercial software developed by Schrödinger, combines empirical and physics-based scoring components . The Molecular Operating Environment (MOE) is another widely used platform, integrating docking with a broad array of computational chemistry tools. MOE's docking module utilizes a force-field-based scoring function . FlexX and Surflex-Dock are two other key programs from the early stage of docking development. FlexX uses an incremental construction algorithm that docks ligands by assembling fragments in the binding site, making it efficient for flexible ligands . Surflex-Dock, with its empirical scoring function, is noted for its speed and user-friendliness . The field of molecular docking and the estimation of protein-ligand binding energies has advanced rapidly over the last 10-15 years, driven by developments in machine learning and artificial intelligence. Alongside empirical and physics-based scoring functions, machine learning-based scoring functions have emerged as a third category. This has led to the development of new scoring functions, program packages, and numerous benchmark studies. Although these advances play important roles in current research, they are not directly relevant to the present work, so only a few representative examples are cited here .