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Updated: Oct 21, 2025

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
Published on: June 4, 2021
A pilot study for fragment identification using 2D NMR and deep learning
Stefan Kuhn1,2, Eda Tumer, Simon Colreavy-Donnelly1
1School of Computer Science and Informatics, De Montfort University, Leicester, UK.
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.
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