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Structure reassignment of two triterpenes with CASE algorithms and DFT chemical shift predictions.

Zhi-Kang DuanTian-Ming LvGuan-Shan SongYu-Xi WangBin LinXiao-Xiao Huang
Published in: Natural product research (2020)
Two triterpenes (14S,17S,20S,24R)-25-hydroxy-14,17-cyclo-20,24-epoxy-malabarican-3-one (CEM, 1a) and (14S,17S,20S,24R)-20,24,25-trihydroxy-14,17-cyclomalabarican-3-one (CM, 2a) with a cyclobutane ring were reported, which have the same NMR data as ocotillone (1b) and gardaubryone C (2b), respectively. An incorrect structure might be reported. Therefore, the structure reanalysis of these triterpenes was achieved by CASE algorithm and DFT chemical shift predictions, and the results showed that the structures of CEM and CM might be incorrect. To further verify the structure of compound 1, the HMBC, 1H-1H COSY and HSQC-TOCSY spectra were employed. Herein, we revised the structure of CEM and CM, and our study also showed that CASE algorithm and DFT chemical shift predictions can hold the post of effective structure reassignment method.
Keyphrases
  • machine learning
  • density functional theory
  • high resolution
  • deep learning
  • big data
  • solid state
  • molecular dynamics
  • neural network