DENSE SENSE : A novel approach utilizing an electron density augmented machine learning paradigm to understand a complex odour landscape

07 May 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Olfaction is a complex process which involves interaction of multiple odour receptors in nasal epithelium to produce the sensation of smell for particular odorant molecules. Elucidating structure-activity-relationships for odorants and their receptors remains difficult since crystallization of the odour receptors extremely difficult. Therefore, ligand-based approaches that leverage machine learning remain the state of the art for predicting odorant properties for molecules, such as the graph neural network approach used by. In this paper we explore how information from Quantum Mechanics (QM) could synergistically improve the results obtained with the graph neural network. Our findings underscore the possibility of this methodology in predicting odour perception directly from QM data, offering a novel approach in the Machine learning space to understand olfaction.

Keywords

Olfaction
Machine learning
Artificial Intelligence
GNN
Featureless learning
Principal odour map
open AI

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.