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Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning.

Daria TorodiiJacob B HolmesPinelopi MoutzouriSten O Nilsson LillManuel CordovaArthur C PinonKristof GroheSebastian WegnerOkky Dwichandra PutraStefan NorbergAnette WelinderStaffan SchantzLyndon Emsley
Published in: Faraday discussions (2024)
The recent development of ultra-fast magic-angle spinning (MAS) (>100 kHz) provides new opportunities for structural characterization in solids. Here, we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of 1 H resolution improvement at ultra-fast MAS and use solely 1 H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to 13 C-detected experiments, without the need to employ dynamic nuclear polarization.
Keyphrases
  • high resolution
  • machine learning
  • crystal structure
  • solid state
  • magnetic resonance
  • artificial intelligence
  • mass spectrometry
  • big data
  • high frequency
  • deep learning
  • healthcare