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Table 1 Validity, uniqueness and novelty (mean ± std) of SMILES generated after training

From: De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

Temperature

Validity (%)

Uniqueness (%)

Novelty (%)

0.20

100.00 ± 0.00

39.79 ± 0.27

33.21 ± 0.59

0.50

99.98 ± 0.03

99.05 ± 0.30

78.44 ± 0.78

0.60

99.95 ± 0.04

99.05 ± 0.18

81.80 ± 1.19

0.70

99.80 ± 0.10

99.58 ± 0.16

85.10 ± 0.58

0.75

99.72 ± 0.15

99.58 ± 0.12

85.85 ± 0.68

0.80

99.44 ± 0.21

99.36 ± 0.20

87.11 ± 0.59

1.00

97.21 ± 0.39

97.15 ± 0.15

88.66 ± 0.95

1.20

89.95 ± 0.23

89.84 ± 0.24

85.38 ± 0.87

  1. We sampled 2000 SMILES for each temperature in five independent runs (10,000 in total)
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BMC Chemistry

ISSN: 2661-801X

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