Molecular docking programs that employ consensus scoring functions integrate the outcomes of various scoring methods to enhance the precision and dependability of fore-casting ligand-receptor interactions ( ). For example, CompScore utilises a consensus scoring function in docking ( ). The other example is CoBDock, which benefits not only molecular docking scoring function but also cavity detection tools to build a consensus approach ( ). The programs utilise consensus scoring to effectively balance the merits and drawbacks of individual scoring systems, thereby improving the overall accuracy of predictions ( ). Consensus scoring offers a significant benefit by effectively decreasing the occurrence of incorrect positive results and enhancing the reliability of forecasts regarding binding affinity. Nevertheless, the drawback is that it frequently necessitates additional computer resources and time, as it involves many scoring calculations that must be done and combined. In addition, the intricacy of including many scoring algorithms might occasionally result in incongruous outcomes if the consensus approach is not optimised ( ). Consequently, our machine learning model underwent training using several scoring function outcomes in order to enhance its performance. Our ML model's method enhances molecular docking accuracy by mitigating false positives.