Machine learning highlights a hidden order in scents

By | October 10, 2022

Allison Parshall writes:

Alex Wiltschko began collecting perfumes as a teenager. His first bottle was Azzaro Pour Homme, a timeless cologne he spotted on the shelf at a T.J. Maxx department store. He recognized the name from Perfumes: The Guide, a book whose poetic descriptions of aroma had kick-started his obsession. Enchanted, he saved up his allowance to add to his collection. “I ended up going absolutely down the rabbit hole,” he said.

More recently, as an olfactory neuroscientist for Google Research’s Brain Team, Wiltschko used machine learning to dissect our most ancient and least understood sense. Sometimes he looked almost longingly at his colleagues studying the other senses. “They have these beautiful intellectual structures, these cathedrals of knowledge,” he said, that explain the visual and auditory world, shaming what we know about olfaction.

Recent work by Wiltschko and his colleagues, however, is helping to change that. In a paper first posted on the biorxiv.org preprint server in July, they described using machine learning to tackle a long-standing challenge in olfactory science. Their findings significantly improved researchers’ ability to compute the smell of a molecule from its structure. Moreover, the way they improved those calculations gave new insights into how our sense of smell works, revealing a hidden order in how our perceptions of smells correspond to the chemistry of the living world. [Continue reading…]

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