  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.
