Molecular Docking (MD) is one of the most common methods to investigate drug-target correlation (Figure ). Regrettably, conventional and ML-based docking methods have been plagued by a significant false-positive rate, leading to limited effectiveness ( ). ML models trained using the outcomes of molecular docking programs can effectively decrease the occurrence of false positives in MD and ML-based docking ( ). Therefore, high false positives reduce the performance of MD. Initially, comprehending the concept of molecular docking is the primary prerequisite for constructing a proficient machine-learning model on molecular docking software. The graphic shows the molecule bonding process. Fundamental to molecular docking is the computer prediction of the binding mechanism and affinity of small molecules (ligands) within the active site of a target protein. This method helps to find possible therapeutic options by assessing the degree of interaction and complementarity between the ligand and the protein target ( ).