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

Weiqi Li, Yinghui Wen, Kaichao Wang, Zihan Ding, Lingfeng Wang, Qianming Chen, Liang Xie (), Hao Xu () and Hang Zhao ()
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Weiqi Li: Sichuan University
Yinghui Wen: Sichuan University
Kaichao Wang: Sichuan University
Zihan Ding: Sichuan University
Lingfeng Wang: Sichuan University
Qianming Chen: Sichuan University
Liang Xie: Sichuan University
Hao Xu: Sichuan University
Hang Zhao: Sichuan University

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69−0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag+ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

Date: 2024
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DOI: 10.1038/s41467-024-46866-9

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