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

This paper presents a method to identify substructures in NMR spectra of mixtures, specifically 2D spectra, 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...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Kuhn, Stefan, Tumer, Eda, Colreavy-Donnelly, Simon, Ricardo Moreira Borges
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.03.2021
Subjects
Artificial neural networks
Computer Science - Artificial Intelligence
Computer Science - Learning
Deep learning
Identification methods
NMR
Nuclear magnetic resonance
Quantitative Biology - Quantitative Methods
Spectra
Substructures
Online AccessGet full text

Cover

More Information
Summary:This paper presents a method to identify substructures in NMR spectra of mixtures, specifically 2D spectra, 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. 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.
ISSN:2331-8422
DOI:10.48550/arxiv.2103.12169