Source data
Li Weiqi (Data collector)
2024
2024/2/28
Switzerland, Geneva
Apache License 2.0
数据描述
Source data of "Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors"
数据列表
1Source Data.xlsx
格式Excel
大小107.2 MB
DOI: 10.5281/ZENODO.10723552
zenodo.org
IP: 137.138.76.77
加载中...
相关论文
2Machine learning-driven discovery of therapeutic nucleoside hydrogels for periodontitis
Weiqi Li•Yinghui Wen•Zhenyuan Huang•Fangyuan Shuai•Yijia Yin等 9 人
International Journal Of Oral Science
2026
•2026/5/11
•Vol.18 No.1 p.410
Supramolecular hydrogels hold significant potential in drug delivery and tissue engineering, with standing out for their unique properties. Despite their promise, predicting nucleoside bioactivity remains challenging. This study aims to predict the biological activity of nucleosides to guide the rat...
Computational chemistryPeriodontitis
10.1038/S41368-026-00438-3
ISSN:2049-3169
Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors
Weiqi Li•Yinghui Wen•Kaichao Wang•Zihan Ding•Lingfeng Wang等 9 人
Nature Communications
2024
•2024/3/23
•Vol.15 No.1 p.1-16
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...
Computational chemistryGels and hydrogelsSelf-assembly
10.1038/S41467-024-46866-9
ISSN:2041-1723