Highly selective and AI-predictable Se–N exchange chemistry between benzoselenazolones and boronic acids for programmable, parallel, and DNA-encoded library synthesis
Year: 2025
Type: article
Abstract: value of 72 nM. Furthermore, a machine learning-based model (SeNEx-ML) was established for reaction yield prediction, achieving 80% accuracy in binary classification and 70% balanced accuracy in ternary classification. These results demonstrated that this chemistry serves as a powerful tool to bridge the selenium chemical space with the existing chemical world, offering transformative potential across multidisciplinary fields.
Source: Chemical Science
DOI: 10.1039/d5sc05512a
Authors: Wei Zhou, Yan Wang, Shuning Zhang, Chengwei Zhang, Jiacheng Pang, Shaoneng Hou, Jie Li, Ying Yao, An Su, Peixiang Ma, Hongtao Xu, Wei Hou
Language: English
Subfield: Toxicology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4415800191