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Table 3 Comparison of the percentage of generated molecules from HierG2G, Seq2Seq and Transformer that are MMPs with starting molecules

From: Molecular optimization by capturing chemist’s intuition using deep neural networks

 

HierG2G

Seq2Seq

Transformer

 

MMP_0.33 (%)

MMP_0.50 (%)

in Train (%)

MMP_0.33 (%)

MMP_0.50 (%)

in Train (%)

MMP_0.33 (%)

MMP_0.50 (%)

in Train (%)

Test-Original

52.50

61.19

45.46

73.55

83.32

43.59

90.45

96.49

48.69

Test-Molecule

25.87

30.98

54.99

67.87

78.48

51.90

88.83

95.58

56.64

Test-Property

13.04

15.20

41.49

72.32

81.77

36.35

90.69

96.37

42.02

  1. Among all the generated molecules for each test set, MMP_0.33 and MMP_0.50 represent the percentage of generated molecules that are MMPs with their corresponding starting molecules and the ratio of heavy atoms in R group to the generated molecule \(R_{group}\leq0.33\) and \(R_{group}\leq0.50\) respectively. Among all the transformations results from MMP_0.33, in Train represents the percentage of transformations that are seen in the training set
  2. For MMP_0.33 and MMP_0.50, higher values are better, and the best values are in italics
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Journal of Cheminformatics

ISSN: 1758-2946

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