Google AI has used artificial intelligence to produce a map that relates smells to the structures of molecules. It is as reliable as a human in describing the odor of a substance and the researchers behind the work say it is a crucial step toward digitizing odors.

A Little About the Model

Mapping molecular structure to odor perception is a critical challenge in olfaction. Here, the Google AI researchers use neural graph networks (GNN) to generate a Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants. The model is as reliable as a human in describing odor quality: on a prospective validation set of 400 novel odorants, the model-generated odor profile more closely matched the trained panel mean (n=15) than the median panelist. Applying simple, interpretable, theoretically-rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.

Smells are produced by molecules that waft through the air, enter our noses, and bind to sensory receptors. Potentially billions of molecules can produce a smell, so figuring out which ones produce which smells is difficult to catalog or predict. Sensory maps can help us solve this problem. Color vision has the most familiar examples of these maps, from the color wheel the team each learns in primary school to more sophisticated variants used to perform color correction in video production. While these maps have existed for centuries, useful maps for smell have been missing, because the smell is a harder problem to crack: molecules vary in many more ways than photons do; data collection requires physical proximity between the smeller and smell, and the human eye only has three sensory receptors for color while the human nose has > 300 for odor. As a result, previous efforts to produce odor maps have failed to gain traction.

In 2019, Google AI developed a graph neural network (GNN) model that began to explore thousands of examples of distinct molecules paired with the smell labels that they evoke, e.g., "beefy", "floral", or "minty", to learn the relationship between a molecule's structure and the probability that such a molecule would have each smell label. The embedding space of this model contains a representation of each molecule as a fixed-length vector describing that molecule in terms of its odor, much as the RGB value of a visual stimulus describes its color.

This time, the team introduced the "Principal Odor Map" (POM), which identifies the vector representation of each odorous molecule in the model's embedding space as a single point in a high-dimensional space. The POM has the properties of a sensory map: first, pairs of perceptually similar odors correspond to two nearby points in the POM (by analogy, red is nearer to orange than to green on the color wheel). Second, the POM enables us to predict and discover new odors and the molecules that produce them.

Google AI discovered that their modeling approach to smell prediction could be used to draw a Principal Odor Map for tackling odor-related problems more generally. This map was the key to measuring smell: it answered a range of questions about novel smells and the molecules that produce them, it connected smells back to their origins in evolution and the natural world, and it is helping us tackle important human-health challenges that affect millions of people. Going forward, it is hoped that this approach can be used to find new solutions to problems in food and fragrance formulation, environmental quality monitoring, and the detection of human and animal diseases.