Scientists are trying to teach AI how to smell
Describing odors can be surprisingly complicated, even for a complex computer.
It’s hard to overstate the power of the nose—research says humans can distinguish more than a trillion odors. This is especially impressive when you remember that each individual odor is a chemical with a unique structure. Experts have been trying to discern patterns or logic in how chemical structure dictates smell, which would make it much easier to synthetically replicate scents or discover new ones. But that’s incredibly challenging—two very similarly structured chemicals could smell wildly different. When identifying smells is such a complicated task, scientists are asking: Can we get a computer to do it?
Smell remains more mysterious to scientists than our senses of sight or hearing. While we can “map” what we see as a spectrum of light wavelengths, and what we hear as a range of sound waves with frequencies and amplitudes, we have no such understanding for smell. In new research, published this month in the journal Science, scientists trained a neural network with 5,000 compounds from two perfumery databases of odorants—molecules that have a smell—and corresponding smell labels like “fruity” or “cheesy.” The AI was then able to produce a “principal odor map” that visually showed the relationships between different smells. And when the researchers introduced their artificial intelligence to a new molecule, the program was able to descriptively predict what it would smell like.
The research team then asked a panel of 15 adults with different racial backgrounds living near Philadelphia to smell and describe that same odor. They found that “the neural network’s descriptions are better than the average panelist, most of the time,” says Alex Wiltschko, one of the authors of the new paper. Wiltschko is the CEO and co-founder of Osmo, a company whose mission is “to give computers a sense of smell” and that collaborated with researchers from Google and various US universities for this work.
“Smell is deeply personal,” says Sandeep Robert Datta, a neurobiology professor at Harvard University. (Datta has previously acted as a nominal advisor to Osmo, but was not involved in the new study.) And so, any research related to how we describe and label smells has to come with the caveat that our perception of smells, and how smells might relate to each other, is deeply entwined with our memories and culture. This makes it difficult to say what the “best” description of a smell even is, he explains. Despite all this, “there are common aspects of smell perception that are almost certainly driven by chemistry, and that’s what this map is capturing.”
It’s important to note that this team is not the first or only to use computer models to investigate the relationship between chemistry and smell perception, Datta adds. There are other neural networks, and many other statistical models, that have been trained to match chemical structures with smells. But the fact that this new AI produced an odor map and was able to predict the smells of new molecules is significant, he says.
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This neural network strictly looks at chemical structure and smell, but that doesn’t really capture the complexity of the interactions between chemicals and our olfactory receptors, Anandasankar Ray, who studies olfaction at the University of California, Riverside, and was not involved in the research, writes in an email. In his work, Ray has predicted how compounds smell based on which of the approximately 400 human odorant receptors are activated. We know that odorant receptors react when chemicals attach to them, but scientists don’t know exactly what information these receptors transmit to the brain, or how the brain interprets these signals. It’s important to make predictive models while keeping biology in mind, he wrote.
Additionally, to really see how general the model could go, Ray points out that the team should have tested their neural network on more datasets separate from the training data. But until they do that, we can’t say how widely useful this model is, he adds.
What’s more, the neural network doesn’t take into account how our perceptions of a smell can change with varying concentrations of odorants. “A really great example of this is a component of cat urine called MMB; it’s what makes cat pee stink” says Datta.” But at very low concentrations, it smells quite appealing and even delicious—it’s found in some coffees and wines. It’ll be interesting to see if future models can take this into account, Datta adds.
Overall, it’s important to note that this principal odor map “doesn’t explain the magic of how our nose sifts through a universe of chemicals and our brain alights on a descriptor,” says Datta. “That remains a profound mystery.” But it could facilitate experiments that help us interrogate how the brain perceives smells.
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Witschko and his collaborators are aware of other limitations of their map. “With this neural network, we’re making predictions on one molecule at a time. But you never smell one molecule at a time—you always smell blends of molecules,” says Witschko. From a flower to a cup of morning coffee, most “smells” are actually a mixture of many different odorants. The next step for the authors will be to see if neural networks can predict how combinations of chemicals might smell.
Eventually, Wiltschko envisions a world where smell, like sound and vision, is fully digitizable. In the future he hopes machines will be able to detect smells and describe them, like speech to text capabilities on smartphones. Or similar to how we can demand a specific song from a smart speaker, they would be able to exude specific smells on demand. But there’s more to be done before that vision becomes reality. On the mission to digitize smell, Wiltschko says, “this is just the first step.”
