Sammanfattning

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof-of-concept of the application of deep learning and neural networks for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.

Nyckelord

artificial intelligence; deep learning; fast sampling; NMR spectroscopy

Publicerad i

Angewandte Chemie International Edition
2020, Volym: 59, nummer: 26, sidor: 10297-10300

SLU författare

• Agback, Tatiana

  • Institutionen för molekylära vetenskaper, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Bioinformatik (beräkningsbiologi)
Analytisk kemi


Publikationens identifierare

DOI: https://doi.org/10.1002/anie.201908162

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/101752