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Table 1 Results of re-ranking four one-step models on the USPTO-50K test dataset

From: Improving the performance of models for one-step retrosynthesis through re-ranking

Models

Top-N accuracy (%)

Mean Reciprocal Rank

1

3

5

10

20

50

RetroSim

35.7 (\(\pm 0\))

53.3 (\(\pm 0\))

62.0 (\(\pm 0\))

73.4 (\(\pm 0\))

82.3 (\(\pm 0\))

88.5 (\(\pm 0\))

0.477 (\(\pm 0.000\))

RetroSim + FF-EBM

49.7 (\(\pm 0.34\))

72.3 (\(\pm 0.21\))

79.4 (\(\pm 0.15\))

85.5 (\(\pm 0.13\))

88.1 (\(\pm 0.07\))

88.9 (\(\pm 0.01\))

0.622 (\(\pm 0.002\))

RetroSim + Graph-EBM

51.8 (\(\pm 0.43\))

74.5 (\(\pm 0.37\))

81.1 (\(\pm 0.17\))

86.4 (\(\pm 0.13\))

88.5 (\(\pm 0.02\))

88.9 (\(\pm 0.00\))

0.644 (\(\pm 0.004\))

NeuralSym

45.7 (\(\pm 0.30\))

66.4 (\(\pm 0.40\))

73.5 (\(\pm 0.30\))

80.7 (\(\pm 0.21\))

85.3 (\(\pm 0.34\))

87.3 (\(\pm 0.32\))

0.578 (\(\pm 0.001\))

NeuralSym + FF-EBM

50.5 (\(\pm 0.21\))

71.8 (\(\pm 0.62\))

78.7 (\(\pm 0.18\))

84.5 (\(\pm 0.32\))

87.1 (\(\pm 0.29\))

87.5 (\(\pm 0.32\))

0.626 (\(\pm 0.003\))

NeuralSym + Graph-EBM

51.3 (\(\pm 0.52\))

73.6 (\(\pm 0.34\))

80.2 (\(\pm 0.35\))

85.4 (\(\pm 0.30\))

87.1 (\(\pm 0.27\))

87.5 (\(\pm 0.32\))

0.636 (\(\pm 0.004\))

RetroXpert

45.8 (\(\pm 0.25\))

59.2 (\(\pm 0.26\))

63.0 (\(\pm 0.57\))

66.9 (\(\pm 0.31\))

69.9 (\(\pm 0.62\))

73.0 (\(\pm 0.70\))

0.543 (\(\pm 0.004\))

RetroXpert + FF-EBM

42.7 (\(\pm 0.27\))

62.0 (\(\pm 0.21\))

67.6 (\(\pm 0.05\))

72.5 (\(\pm 0.08\))

75.6 (\(\pm 0.11\))

77.1 (\(\pm 0.20\))

0.536 (\(\pm 0.002\))

RetroXpert + Graph-EBM

36.7 (\(\pm 0.91\))

58.2 (\(\pm 1.06\))

65.8 (\(\pm 0.73\))

73.0 (\(\pm 0.32\))

75.9 (\(\pm 0.12\))

77.3 (\(\pm 0.21\))

0.491 (\(\pm 0.008\))

GLN

51.7 (\(\pm 0.33\))

67.8 (\(\pm 0.43\))

75.1 (\(\pm 0.32\))

83.2 (\(\pm 0.12\))

88.9 (\(\pm 0.11\))

92.4 (\(\pm 0.06\))

0.620 (\(\pm 0.003\))

GLN + FF-EBM

49.7 (\(\pm 0.77\))

72.4 (\(\pm 0.18\))

80.0 (\(\pm 0.28\))

87.0 (\(\pm 0.11\))

90.6 (\(\pm 0.12\))

93.0 (\(\pm 0.02\))

0.629 (\(\pm 0.005\))

GLN + Graph-EBM

52.3 (\(\pm 0.01\))

74.9 (\(\pm 0.27\))

82.0 (\(\pm 0.18\))

88.0 (\(\pm 0.02\))

91.4 (\(\pm 0.11\))

93.0 (\(\pm 0.08\))

0.652 (\(\pm 0.001\))

  1. Bolded values refer to the best top-N accuracy and the best MRR for that one-step model. We report the average of 3 experiments where both the proposer and re-ranker are initialized with a different random seed, with the standard deviation in parentheses. Note that RetroSim is a deterministic algorithm and is reported with a standard deviation of 0
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Journal of Cheminformatics

ISSN: 1758-2946

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