The scoring functions in molecular docking programs are essential in computational drug discovery and the research of protein-ligand interactions ( ). It is a mathematical model employed to evaluate and prioritise the strength of the interaction between a tiny chemical (a ligand) and a target protein receptor. The scoring function assesses the potency of the ligand-receptor interaction, forecasting the probability of a favourable binding position. This forecast is crucial for the identification of possible therapeutic candidates or the comprehension of protein-ligand interaction mechanisms ( ). A comprehensive scoring function considers multiple aspects, including van der Waals contacts, electrostatic interactions, hydrogen bonding, solvation effects, and entropy variations. Scoring functions that utilise physical force fields (or force fields) are employed to analyse molecular interactions (Figure ). The approach integrates molecular dynamics (MD), binding affinity, and free energy perturbation (FEP) methods. Medusa Score, for example, is one of the physical force field-based approaches. The research demonstrated that the Medusa Score success rate is around 82% ( ). The success rate is better than various standard scoring functions, including DrugScore, F-Score, LigScore, ChemScore, PLP, LUDI, PMF, X-Score, G-Score, D-Score, and AutoDock. When the scoring method was hybridised with DrugScore, it became 85% ( ). However, the drawbacks of techniques are speed and sampling limitations ( ).