AI rivals the human nose when it comes to naming smells

You have to smell it to believe it! 😊 This is only the beginning. 

What will we find out about pecunia non olet (money does not stink)? Just kidding. Or do you smell a rat?

This Google research dates back to 2019. 

"... Today [9/6/2022 (yes exactly one year ago)] we introduce 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. In a series of papers, we demonstrate that the map can be used to prospectively predict the odor properties of molecules, understand these properties in terms of fundamental biology, and tackle pressing global health problems. ..."

"... The results, reported today in Science, show that the program, a so-called graph neural network, is excellent at imitating human sniffers, at least when it comes to simple odors. It reliably predicted what the volunteers smelled, a feat sensory biologists have been working toward for decades. It also predicted the smells of 500,000 other molecules, with no need to make or sniff them. ...

The findings may also help establish olfactory research as a field on par with sight or vision. Smell, which in humans involves a smaller proportion of the brain and fewer types of receptor cells than in other mammals, was long considered “a primitive sense that wasn’t worth studying by neurobiologists,” ... It has also defied systematic study. Whereas what we see and hear reflects quantifiable properties such as wavelength and frequency, smell doesn’t neatly correspond with the shape of a molecule. Similarly structured molecules can smell different, whereas dissimilar molecules can produce the same odor. ..."

From the editor's summary and abstract:

"Editor's summary

For vision and hearing, there are well-developed maps that relate physical properties such as frequency and wavelength to perceptual properties such as pitch and color. The sense of olfaction does not yet have such a map. Using a graph neural network, Lee et al. developed a principal odor map (POM) that faithfully represents known perceptual hierarchies and distances. This map outperforms previously published models to the point that replacing a trained human’s responses with the model output would improve overall panel description. The POM coordinates were able to predict odor intensity and perceptual similarity, even though these perceptual features were not explicitly part of the model training. These results were used to build a variety of olfactory predictions that outperformed previous feature sets even without fine-tuning. Abstract

Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By 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."

AI rivals the human nose when it comes to naming smells | Science | AAAS

Digitizing Smell: Using Molecular Maps to Understand Odor

A principal odor map unifies diverse tasks in olfactory perception (no public access)

Left: An example of a color map (CIE 1931) in which coordinates can be directly translated into values for hue and saturation. Similar colors lie near each other, and specific wavelengths of light (and combinations thereof) can be identified with positions on the map. Right: Odors in the Principal Odor Map operate similarly. Individual molecules correspond to points (grey), and the locations of these points reflect predictions of their odor character.