Wei Zhou, Yan Wang, Shuning Zhang, Chengwei Zhang, Jiacheng Pang, Shaoneng Hou, Jie Li, Ying Yao, An Su, Peixiang Ma, Hongtao Xu, Wei Hou

Chemical Science 

DOI: 10.1039/d5sc05512a

Abstract

Chemical reactions compatible with multiple functionalities are essential for rapid, programmable, and automatable synthesis of functional molecules. However, achieving such reactivity poses significant challenges. Here, we developed a novel multi-orthogonal C(sp 2 )-Se bond formation reaction between benzoselenazolones and boronic acids via Ag(I)catalyzed selective selenium(II)-nitrogen exchange. This chemistry is compatible with diverse functionalities, enabling sequential and programmable synthesis. Moreover, it features modular, high-yielding (485 examples, with yields or conversions exceeding 70% in 95% cases), and switchable reaction systems under mild conditions. Its practical utility was exemplified through late-stage functionalization of natural products, peptides modification and ligation, diversified synthesis, sequential click chemistry, protecting group-free syntheses of sequence-defined oligoselenide (nonamer), on-plate nanomole-scale parallel synthesis (200 nmol, 412 selenides), and DNA-encoded library (DEL) synthesis (10 nmol, 92 examples). Notably, a target-based screening identified SA-16 as a potent CAXII inhibitor with an IC 50 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.

Summary

This article presents a novel, highly selective, and AI-predictable selenium(II)-nitrogen exchange (SeNEx) chemistry between benzoselenazolones and boronic acids. The Ag(I)-catalyzed reaction forms C(sp²)-Se bonds and is compatible with a wide range of functionalities, making it suitable for programmable and parallel synthesis. The chemistry is modular, high-yielding, and operates under mild conditions. It has been successfully applied in various practical syntheses, including late-stage functionalization of natural products, peptide modifications, and DNA-encoded library synthesis. A machine learning model (SeNEx-ML) was also developed to predict reaction yields with high accuracy.

Highlights

• Development of a novel Se-N exchange chemistry between benzoselenazolones and boronic acids.
• The reaction is highly selective, modular, and high-yielding, with 95% of 485 examples achieving yields or conversions exceeding 70%.
• Compatibility with diverse functionalities and orthogonal to other top reactions in medicinal chemistry.
• Successful application in late-stage functionalization, peptide modification, and DNA-encoded library synthesis.
• Establishment of a machine learning model (SeNEx-ML) for reaction yield prediction with high accuracy.

Conclusion

In summary, we have successfully designed and developed an unprecedented highly selective and multi-orthogonal SeNEx chemistry between benzoselenazolones and boronic acids. This chemistry features modular, predictable, robust, and high-yielding characteristics, performed under mild and switchable reaction conditions. It demonstrates exceptional chemo-selectivity and functional group tolerance, enabling orthogonal synthesis with other established reactions. The practical applications in late-stage modification, peptide ligation, and DNA-encoded library synthesis highlight its potential in multidisciplinary fields. The establishment of the SeNEx-ML model further enhances its utility by predicting reaction outcomes accurately. This chemistry serves as a powerful tool to bridge the selenium chemical space with the existing chemical world, offering transformative potential in synthetic chemistry, material science, chemical biology, and medical chemistry.