# Direct deduction of chemical class from NMR spectra

arXiv: https://arxiv.org/abs/2211.03173 · HTML: https://mlsanity.com/paper/2211.03173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 6 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [A Pilot Study For Fragment Identification Using 2D NMR and Deep Learning](https://mlsanity.com/paper/2103.12169/) (2021)

## Abstract (arXiv)

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. This can help to reduce time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. Therefore, for a long time automation in the area of NMR has been actively sought. The method identified as suitable for the classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to automation problems in cheminformatics.