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Table 2 Comparison architectures A, B, C and D

From: GEN: highly efficient SMILES explorer using autodidactic generative examination networks

Architecture

Merge mode

Layer count

Layer size

Best model epoch#

Validity%

Uniqueness%

Training%

Length match%a

HAC match%b

A: LSTM–LSTM

1/1

64/64

54, 72, 63

95.4 ± 0.4

99.9 ± 0.1

12.0 ± 0.9

98.2 ± 0.3

94.0 ± 0.9

B: biLSTM–biLSTM

1/1

64/64

20, 22, 28

96.5 ± 0.5

99.9 ± 0.1

12.5 ± 0.9

97.9 ± 0.5

94.9 ± 0.8

A: LSTM–LSTM

1/1

256/256

17, 17, 20

96.7 ± 0.4

99.9 ± 0.1

15.0 ± 0.7

98.2 ± 0.9

94.0 ± 1.8

B: biLSTM–biLSTM

1/1

256/256

6, 7, 10

97.1 ± 0.4

99.9 ± 0.1

13.1 ± 0.5

98.2 +/0.6

93.9 ± 0. 8

C: biLSTM–biLSTM

Concatenated

1/4

64/64

10, 14, 16

97.0 ± 0.3

99.9 ± 0.0

11.9 ± 0.6

98.5 ± 0.3

97.4 ± 0.5

C: biLSTM–biLSTM

Average

1/4

64/64

11, 15, 15

97.2 ± 0.3

99.9 ± 0.1

12.5 ± 0.3

98.6 ± 0.2

96.1 ± 0.7

C: biLSTM–biLSTM

Learnable average

1/4

64/64

15, 17, 23

97.6 ± 0.2

99.9 ± 0.0

14.6 ± 0.2

97.4 ± 0.4

94.8 ± 1.2

D: biLSTM–biLSTM

Concatenated

4/4

64/64

11, 11, 9

96.9 ± 0.3

99.9 ± 0.0

14.4 ± 0.5

97.4 ± 0.2

95.6 ± 1.2

D: biLSTM–biLSTM

Average

4/4

64/64

15, 17, 14

96.7 ± 0.1

99.9 ± 0.0

11.9 ± 0.2

98.1 ± 0.5

95.3 ± 1.1

D: biLSTM–biLSTM

Learnable average

4/4

64/64

12, 25, 18

95.6 ± 0.1

99.9 ± 0.0

10.4 ± 0.5

98.0 ± 0.2

96.2 ± 0.6

Influence of bidirectionality

 LSTM–LSTM

Concatenated

1/4

64/64

20,17,31

96.8 ± 0.4

99.9 ± 0.1

13.4 ± 0.5

97.6 ± 0.8

94.8 ± 1.3

 biLSTM-LSTM

Concatenated

1/4

64/64

9, 14, 9

97.1 ± 0.3

99.9 ± 0.1

13.2 ± 0.5

97.7 ± 0.9

95.5 ± 1.4

  1. Best architecture is highlighted in italics
  2. aLength match for SMILES length distributions of the training set and generated set (See “Methods”)
  3. bHAC match for the atom count distributions of the generated set and training set (See “Methods”)