A Pilot Study For Fragment Identification Using 2D NMR and Deep Learning

Date

2021-09-04

Advisors

Journal Title

Journal ISSN

ISSN

0749-1581

Volume Title

Publisher

Wiley

Type

Article

Peer reviewed

Yes

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.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

NMR, Structure Elucidation, Deep Learning, Convolutional Neural Network, Image Processing

Citation

Kuhn, S., Tumer, E., Colreavy-Donnelly, S., Borges, R.M. (2021) A Pilot Study For Fragment Identification Using 2D NMR and Deep Learning. Magnetic Resonance in Chemistry,

Rights

Research Institute

Cyber Technology Institute (CTI)