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

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.