Olfactory Label Prediction on aroma-chemical Pairs
Laura Sisson
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 odorants. In this
paper, we apply both existing and novel approaches to a dataset we gathered
consisting of labeled pairs of molecules. We present a publicly available model
capable of generating accurate predictions for the non-linear qualities arising
from blends of aroma-chemicals.