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

Abstract

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.

arXiv comment11 pages, 3 figures, 3 tables

References in corpus (1)

• Do We Need More Training Data?

Cited by in corpus (1)

• Direct deduction of chemical class from NMR spectra