Table 5 Overall pose prediction performance of AutoDock Vina, PSOVina, PSOVina\(^{{\mathrm{2LS}}}\), chaos-embedded PSOVina\(^{{\mathrm{2LS}}}\) methods
From: Chaos-embedded particle swarm optimization approach for protein-ligand docking and virtual screening
Best-scoring pose RMSD (Å) | Best-scoring pose success rate (%) | Run time (s) | |
|---|---|---|---|
AutoDock Vina | 2.50 (0.40) | 65.68 (4.17) | 17.56 (5.24) |
PSOVina | 2.27 (0.33) | 68.56 (3.86) | 9.40 (2.31) |
PSOVina\(^{{\mathrm{2LS}}}\) | 2.11 (0.37) | 70.89 (3.79) | 2.96 (0.36) |
Chaos-embedded PSOVina\(^{2LS}\) | |||
Logistic map | 2.08 (0.36) | 72.72 (4.57) | 3.21 (0.41) |
Singer map | 2.06 (0.37) | 73.21 (3.06) | 3.26 (0.35) |
Sinusoidal map | 1.96 (0.34) | 74.62 (4.08) | 3.33 (0.35) |
Tent map | 2.01 (0.31) | 71.85 (4.67) | 3.08 (0.37) |
Zaslavskii map | 2.08 (0.41) | 72.48 (5.21) | 3.09 (0.47) |