Highly selective and AI-predictable Se–N exchange chemistry between benzoselenazolones and boronic acids for programmable, parallel, and DNA-encoded library synthesis
Wei Zhou, Yan Wang, Shuning Zhang, Chengwei Zhang, Jiacheng Pang, Shaoneng Hou, Jie Li, Yao Ying, An Su, Peixiang Ma, Hongtao Xu, Wei Hou
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.
Topics & Concepts
ChemistryCombinatorial chemistryChemical synthesisBoronic acidTernary operationReactivity (psychology)Click chemistryYield (engineering)Chemical spaceOrganic chemistrySurface modificationReaction conditionsNanotechnologyCascade reactionPeptide synthesisChemical modificationGreen chemistryAmino acidSeleniumOrganoselenium and organotellurium chemistryOrganoboron and organosilicon chemistryCatalytic Cross-Coupling Reactions