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HTE- and AI-assisted development of DHP-catalyzed decarboxylative selenation.

Zhunzhun YuYaxian KongBaiqing LiShimin SuJianhang RaoYadong GaoTianyong TuHongming ChenKuangbiao Liao
Published in: Chemical communications (Cambridge, England) (2023)
1,4-Dihydropyridine (DHP) derivatives play key roles in biology, but are rarely used as catalysts in synthesis. Here, we developed a DHP derivative-catalyzed decarboxylative selenation reaction that showed a broad substrate scope, with the assistance of high-throughput experimentation (HTE) and artificial intelligence (AI). The AI-based model could identify the key structural features and give accurate prediction of unseen reactions ( R 2 = 0.89, RMSE = 9.0%, and MAE = 6.3%). Our work not only developed the catalytic applications of DHP derivatives, but also demonstrated the power of the combination of HTE and AI to advance chemical synthesis.
Keyphrases
  • artificial intelligence
  • machine learning
  • big data
  • deep learning
  • high throughput
  • room temperature
  • visible light
  • mass spectrometry
  • highly efficient
  • crystal structure
  • water soluble