  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 which is able to
achieve good results with comparatively low amounts of data. We show this by
predicting 19F and 13C NMR chemical shifts of small molecules in specific
solvents.
