A Pilot Study For Fragment Identification Using 2D NMR and Deep Learning
This addresses the challenge of substructure identification in NMR analysis for chemistry, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of identifying substructures in NMR spectra of mixtures using a custom image-based Convolutional Neural Network, achieving better results with HMBC data and combined HMBC-HSQC spectra compared to HSQC alone.
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.