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

• URL: http://arxiv.org/abs/2103.12169v1
• Date: Thu, 18 Mar 2021 20:25:41 GMT
• Title: A Pilot Study For Fragment Identification Using 2D NMR and Deep Learning
• Authors: Stefan Kuhn, Eda Tumer, Simon Colreavy-Donnelly, Ricardo Moreira Borges
• Abstract summary: The application can reliably detect substructures in pure compounds, using a simple network. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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