POMMix, a graph-based model with attention and cosine similarity heads, extends the Principal Odor Map to predict human perceptual similarity of odor mixtures, reporting a test correlation of 0.78 on a compiled dataset of 865 mixture pairs.
Olfactory Label Prediction on Aroma-Chemical Pairs
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abstract
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.
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
POMMix, a graph-based model with attention and cosine similarity heads, extends the Principal Odor Map to predict human perceptual similarity of odor mixtures, reporting a test correlation of 0.78 on a compiled dataset of 865 mixture pairs.