Olfactory Label Prediction on Aroma-Chemical Pairs

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, perfum...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Sisson, Laura, Aryan, Amit Barsainyan, Sharma, Mrityunjay, Kumar, Ritesh
Format: Article
Language:English
Subjects:
Aroma
Industrial applications
Mixtures
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422