This paper presents the development of a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model, based on a dataset of 71 nucleoside derivatives, achieved 71% accuracy. Experimental verification of 24 molecules selected by the model revealed two novel cation-independent nucleoside hydrogels with potential applications in Ag+ and cysteine detection.
