NMR shift prediction from small data quantities

Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model that is able to achieve better results than other models for relevant datasets with comparatively low amounts of data. We show this by predicting F-19 and C-13 NMR chemical shifts of small molecules in specific solvents.

Automated summary

This study demonstrates a novel machine learning model that can achieve accurate prediction of F-19 and C-13 NMR chemical shifts of small molecules in specific solvents, even with relatively limited data.