A pilot study for fragment identification using 2D NMR and deep learning.

Journal: Magnetic resonance in chemistry : MRC
Published Date: Sep 21, 2021

Abstract

This paper presents a proof of concept of a method to identify substructures in 2D NMR spectra of mixtures using a bespoke image-based convolutional neural network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. Results indicate that it can work for mixtures when trained on pure compounds only. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone in this pilot study.

Authors

  • Stefan Kuhn
    School of Computer Science and Informatics, De Montfort University, Leicester, UK.
  • Eda Tumer
  • Simon Colreavy-Donnelly
    School of Computer Science and Informatics, De Montfort University, Leicester, UK.
  • Ricardo Moreira Borges
    Instituto de Pesquisas de Produtos Naturais Walter Mors, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.

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