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Table 1 Computational prediction and experimental verification of the 10 newly synthesized molecules

From: Machine learning approaches to optimize small-molecule inhibitors for RNA targeting

  Predicted binding score, model
R1 R2 X Molecule (Zinc#) Lassoa CNN-SMILESa CNN-pictoriala Decision treeb Docking valuesa Ribosome activity (%)c IC-50 (μM)
1 H H CO 32048999 − 8.53 − 7.92 − 8.18 II − 10.75 100 No inhibition
2 H CH3 CH2 22780988 − 12.67 − 10.04 − 14.75 I − 13.77 3.07 12.6
3 H COCH3 CO 44442665 − 9.93 − 7.97 − 8.18 II − 7.49 100 No inhibition
4 H CH2CH3 CH2 19595411 − 14.05 − 13.81 − 14.96 I − 13.73 1.19 9.1
5 H Bn CO 2992714 − 11.16 − 9.30 − 7.33 II − 10.43 29.10 Weak inhibitor
6 H Bn CH2 22455064 − 13.91 − 13.90 − 9.89 II − 15.29 2.21 9.77
7 H Boc CH2 914572967 − 10.55 − 9.83 − 12.96 II − 10.26 69.87 Weak inhibitor
8 H H CH2 19944337 − 13.76 − 13.97 − 9.30 I − 14.27 51.88 Weak inhibitor
9 H COCH3 CH2 23218050 − 11.33 − 9.78 − 13.38 II − 9.90 8.38 97.27
10 Cl Boc CO 24030014 − 9.12 − 8.11 − 8.29 II − 7.88 35.72 Weak inhibitor
Inhibitors recall 1.00 0.75 0.50 0.75    
Precision 1.00 1.00 0.50 1.00    
Accuracy 1.00 0.90 0.70 0.90    
  1. aNegative score corresponds to stronger binding; bold—effective inhibitor, italics—weak inhibitor
  2. bGroup classification: binding (group I) > binding (group II), groups are based on decision tree classifier (Fig. 3)
  3. cActivity measured in the presence of 1 mM of the compound and M. smegmatis ribosomes in a bacterial coupled transcription/translation assay