Olfactory Label Prediction on Aroma-Chemical Pairs

Laura Sisson, Aryan Amit Barsainyan, Mrityunjay Sharma, Ritesh Kumar
12/26/2023
cs.LGphysics.chem-phq-bio.QM

No official code found. Best implementation: odor-pair/odor-pair (16 stars, MIT, last commit 2025-10-01).

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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Code Implementations(1)

odor-pair/odor-pairCommunity100%
MIT

Source code for "Olfactory Label Prediction on Aroma-Chemical Pairs"

163Python, Jupyter NotebookMay 3, 20241 years agoMIT

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Cite this paper

@article{sisson2023olfactory,
  title  = {Olfactory Label Prediction on Aroma-Chemical Pairs},
  author = {Laura Sisson and Aryan Amit Barsainyan and Mrityunjay Sharma and Ritesh Kumar},
  year   = {2023},
  eprint = {2312.16124},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.16124v2}
}

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