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Table 4 Docking performance comparison of AutoDock Vina, PSOVina, PSOVina\(^{{\mathrm{2LS}}}\), and chaos-embedded PSOVina\(^{{\mathrm{2LS}}}\) methods on four pose prediction datasets

From: Chaos-embedded particle swarm optimization approach for protein-ligand docking and virtual screening

  Best-scoring pose RMSD (Å)\(^{\mathrm{a}}\) Average RMSD (Å) Best-scoring pose success rate (%)\(^{\mathrm{a}}\) Average success rate (%) No. of iterations\(^{\mathrm{b}}\) Run time (s)\(^{\mathrm{b}}\)
(a) PDBBind v.2014 dataset
 AutoDock Vina 2.68393 2.70336 62.56 61.33 22777 21.46
 PSOVina 2.27188 2.50727 68.21 64.67 892 8.97
 PSOVina\(^{{\mathrm{2LS}}}\) 2.14915 2.79023 70.77 61.03 957 3.43
 Chaos-embedded PSOVina\(^{2LS}\)
  Logistic map 1.95241 2.61573 72.82 63.49 1053 3.75
  Singer map 1.98661 2.52277 72.82 64.26 1069 3.75
  Sinusoidal map 1.90650 2.73205 74.36 61.33 1105 3.82
  Tent map 2.07797 2.77287 69.23 60.92 981 3.54
  Zaslavskii map 1.98789 2.65951 72.31 62.00 1015 3.67
(b) Astex diverse dataset
 AutoDock Vina 1.90681 1.92633 71.76 71.53 20086 18.53
 PSOVina 1.82160 1.71506 74.12 76.35 1392 8.21
 PSOVina\(^{{\mathrm{2LS}}}\) 1.58374 1.87782 75.29 72.59 885 2.63
 Chaos-embedded PSOVina\(^{2LS}\)
  Logistic map 1.63183 1.90169 76.47 71.65 951 2.82
  Singer map 1.61686 1.88862 77.65 72.35 1097 3.05
  Sinusoidal map 1.50551 1.99939 80.00 71.06 1234 3.30
  Tent map 1.54835 1.91905 78.82 72.12 968 2.85
  Zaslavskii map 1.54228 1.84950 78.82 72.12 928 2.72
(c) GOLD benchmark set
 AutoDock Vina 2.78586 2.91744 64.94 63.25 20071 19.91
 PSOVina 2.59811 2.58979 66.23 66.75 1289 7.64
 PSOVina\(^{{\mathrm{2LS}}}\) 2.41496 2.85823 71.43 60.91 897 2.75
 Chaos-embedded PSOVina\(^{2LS}\)
  Logistic map 2.32352 2.71251 75.32 64.42 1002 2.97
  Singer map 2.50710 2.73068 71.43 62.73 990 2.97
  Sinusoidal map 2.27549 2.61833 74.03 64.81 1065 3.15
  Tent map 2.23369 2.69675 70.13 62.60 916 2.72
  Zaslavskii map 2.45169 2.80725 72.73 62.73 866 2.69
(d) SB2012 docking validation dataset
 AutoDock Vina 2.64185 2.77003 63.47 61.79 22977 20.33
 PSOVina 2.38248 2.64763 65.68 62.78 1372 12.77
 PSOVina\(^{{\mathrm{2LS}}}\) 2.29462 2.91399 66.06 58.12 1036 3.04
 Chaos-embedded PSOVina\(^{2LS}\)
  Logistic map 2.41665 2.91596 66.25 57.94 1112 3.31
  Singer map 2.11773 2.94298 70.95 57.48 1138 3.25
  Sinusoidal map 2.16409 3.08916 70.09 54.67 1133 3.03
  Tent map 2.17928 2.99936 69.22 56.74 1066 3.21
  Zaslavskii map 2.35440 2.94977 66.06 57.17 1081 3.27
  1. \(^{\mathrm{a}}\) The best-scoring pose is the pose with the lowest binding affinity in docking repeats. Thus, best-scoring pose RMSD and success rate are the average RMSD and success rate of the best-scoring poses of all complexes in the dataset
  2. \(^{\mathrm{b}}\) No. of iterations and run time were averaged from all docking instances
  3. Best results are shown in italics