# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 3 Results
### Evaluation of the Performance of AutoDock and Vina
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Vina**} & \multicolumn{2}{c}{**AutoDock**} \\ \cline{2-5}
**Target Size (Number of aa)** & **EF1\%** & **AUC** & **EF1\%** & **AUC** \\ \hline Small (0–250 aa) & \(8.6\pm 10.1\) & \(70.7\pm 11.0\) & \(9.4\pm 12.3\) & \(66.6\pm 16.0\) \\ Medium (250–400 aa) & \(11.5\pm 10.0\) & \(71.8\pm 12.5\) & \(7.9\pm 9.7\) & \(64.3\pm 15.8\) \\ Large (>400 aa) & \(6.1\pm 7.0\) & \(65.4\pm 11.0\) & \(8.9\pm 10.6\) & \(65.8\pm 18.2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of Vina and AutoDock in the discrimination between ligands and decoys for the targets of different sizes based on the number of amino acid residues of each target (number of aa). Figure 4: Average AUC calculated for the various metalloenzymes, obtained with Vina and AutoDock. Figure 3: Average AUC calculated for the different family targets, obtained with Vina and AutoDock. The results from Table 3 show that Vina was, on average, better in discriminating ligands from decoys in medium-sized targets, with 250 to 400 amino acid residues (average EF1% of 11.5, AUC 71.8). For targets with more than 400 amino acid residues, the performance of Vina was significantly lower (average EF1% of only 6.1, AUC of 65.4)  
AutoDock exhibited a more uniform behavior, with average EF1% values in the range 7.9-9.4 for small (less than 250 aa) and large targets (more than 400 aa), resulting in an improved performance over Vina for the small targets (<250 aa) and the large targets (>400 aa). Another important aspect regarding the nature of the target protein concerns the type of amino acid residues that constitute each binding pocket. For this analysis, all amino acid residues defining each binding pocket were grouped into polar, charged (negative and positive), and hydrophobic amino acid residues. Binding pockets were characterized based on the relative percentage of each of these types of residues. Average EF1% and AUC values were calculated with AutoDock and Vina for each category. The results are presented in Table 4.  
The results presented in Table 4 showed that for poorly polar binding pockets (less than 25% of polar residues) AutoDock was on average better than Vina in discriminating between ligands and decoys, particularly among the top 1% of ranked solutions. For moderately polar and very polar binding pockets, Vina exhibited a better performance than AutoDock. The results also showed that both programs had more difficulty in discriminating ligands and decoys for very polar binding pockets (>35% of polar amino acid residues). In terms of the percentage of hydrophobic residues, the results showed that Vina was significantly better than AutoDock in ligand/decoy discrimination for poorly hydrophobic binding pockets. As the percentage of hydrophobic residues at the binding pocket increased, the performance of Vina and AutoDock became increasingly similar, both in terms of EC1% and in terms of AUC values. In terms of charge, the results showed that AutoDock was better in discriminating ligands and decoys in poorly charged binding pockets (<15%) than in moderate or highly charged ones. Vina, on the other hand, gave best results in highly charged binding pockets. These general tendencies