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

Chemistry

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

W. Li, Y. Wen, et al.

Discover the groundbreaking research by Weiqi Li, Yinghui Wen, Kaichao Wang, Zihan Ding, Lingfeng Wang, Qianming Chen, Liang Xie, Hao Xu, and Hang Zhao on predictive machine learning models for hydrogel-forming nucleoside derivatives. With a 71% accuracy rate, their model led to the development of two novel cation-independent nucleoside hydrogels, showing immense potential for Ag+ and cysteine detection.

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Playback language: English
Abstract
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.
Publisher
Nature Communications
Published On
Mar 23, 2024
Authors
Weiqi Li, Yinghui Wen, Kaichao Wang, Zihan Ding, Lingfeng Wang, Qianming Chen, Liang Xie, Hao Xu, Hang Zhao
Tags
machine learning
hydrogel
nucleoside derivatives
cations
detection

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