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

作者：Weiqi Li, Yinghui Wen, Kaichao Wang, Zihan Ding, Lingfeng Wang, Qianming Chen, Liang Xie, Hao Xu, Hang Zhao · 发表于：Nature Communications · 年份：2024 · DOI：10.1038/s41467-024-46866-9 · 被引用次数：60 · 研究领域：Advanced biosensing and bioanalysis techniques、Supramolecular Self-Assembly in Materials、Click Chemistry and Applications

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.