Fig. 4: Prediction and verification of untested nucleoside derivatives. | Nature Communications

Fig. 4: Prediction and verification of untested nucleoside derivatives.

From: Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors

Fig. 4

a 24 nucleoside derivatives were selected (12 high probability and 12 low probability) in a relatively homogeneous manner based on our experience and the costs of obtaining and synthesizing nucleoside derivatives. b 12 nucleoside derivatives with high probability of hydrogel-forming ability were selected. The result shows 10 nucleoside derivatives (1, 3, 4, 6, 7, 8, 9, 10, 11, and 12) formed hydrogels, while the two others (2 and 5) did not. 1, 1-[3,4-Dihydroxy-5- (hydroxymethyl) oxolan-2-yl]−1,3,5-triazinane-2,4,6-trione, DTT; 2, xanthosine, XTS; 3, guanine 5’-monophosphate, GMP; 4, inosine 5’-monophosphate, IMP; 5, 5-fluorouridine, 5-FUR; 6, 8-aminoguanosine, 8-AG; 7, 2’-deoxyguanosine 5’-monophosphate, dGMP; 8, 8-hydroxyguanosine, 8-OHG; 9, 8-azaguanosine, 8-azaG, 10, inosine-5’-carboxylic acid; I-5’-CA; 11, 2’-amino-2’-deoxyguanosine, 2’-NH2-dG, and 12, 2’-O-methylguanosine, 2’-OMe-dG.

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