  The application of deep learning techniques on aroma-chemicals has resulted
in models more accurate than human experts at predicting olfactory qualities.
However, public research in this domain has been limited to predicting the
qualities of single molecules, whereas in industry applications, perfumers and
food scientists are often concerned with blends of many molecules. In this
paper, we apply both existing and novel approaches to a dataset we gathered
consisting of labeled pairs of molecules. We present graph neural network
models capable of accurately predicting the odor qualities arising from blends
of aroma-chemicals, with an analysis of how variations in architecture can lead
to significant differences in predictive power.
