According to the previous benchmark, Autodock Vina (Vina) achieved a very high successful-docking rate, p ̂, but give a rather a low correlation coefficient, R, for binding affinity with respect to experiment. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is a main objective of docking simulations. The accuracy of Vina likely depends on the empirical parameters, which include the Gaussian steric interaction, repulsion, hydrophobic, hydrogen bond, and rotation metrics. In this context, we evaluated the dependence of Vina accuracy upon empirical parameters. Although changing of six parameters alters the obtained R values, the gauss2 and rotation terms form more effects. The p ̂ terms are sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Therefore, three sets of empirical parameters were proposed as well as more to modestly focus on R (set1), modestly focus on p ̂ (set3), and keep a tradeoff between R and p ̂. The testing study over 800 complexes indicated that the Vina with proposed sets of parameters can provide more accurate results since forming the larger R value (R_set1=0.556±0.025) compared with the original one (R_Default=0.493±0.028) and Vina version 1.2 (R_(Vina 1.2)=0.503±0.029). Besides, the testing study over 48 biological targets indicated that the modified Vina can be applied more widely compared with the default package. These newly proposed parameters achieved a higher correlation coefficient and reasonable correlation coefficients (R>0.500) for at least 32 targets, whereas the default parameters provided by the original Vina gave only 31 targets with at least 0.500 correlation. In addition, validation calculations for 1315 complexes obtained from the version 2019 of PDBbind refined structures suggested that set1 of parameters are more appropriate than the other parameters (R_set1=0.621±0.016) compared with the default package (R_Default=0.552±0.018) and Vina version 1.2 (R_(Vina 1.2)=0.549±0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes probably enhance the ranking of ligand-binding affinity using Autodock Vina.