Olfactory Label Prediction on Aroma-Chemical Pairs
This work addresses a practical need for perfumers and food scientists in industry applications, but it is incremental as it extends existing deep learning techniques to paired molecules.
The paper tackled the problem of predicting olfactory qualities for blends of aroma-chemicals, rather than single molecules, and achieved accurate predictions using graph neural network models with analysis of architectural variations.
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.