Predicting the relationship between a molecules structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR) modeling, is an important challenge in chemistry, impacting human nutrition, manufacture of synthetic fragrance, the environment, and sensory neuroscience. We propose the use of graph neural networks for QSOR, and show they significantly out-perform prior methods on a novel data set labeled by olfactory experts. Additional analysis shows that the learned embeddings from graph neural networks capture a meaningful odor space representation of the underlying relationship between structure and odor, as demonstrated by strong performance on two challenging transfer- learning tasks. Machine learning has already had a large impact on the senses of sight and sound. Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.
Olfactory Perception in Relation to the Physicochemical Odor Space
At the 2021 European Chemoreception Organization (ECRO) Conference, which was held from 14th to 17th September in Cascais, Lissabon, we presented our recent work “Olfactory Perception in Relation to the Physicochemical Odor Space” (Bierling et Read more…