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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
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Journal of Cheminformatics

ISSN: 1758-2946

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