“Now the Machines Are Learning How to Smell: Google researchers are training neural networks with a new technique to predict how a molecule smells based on its chemical structure.”
WIRED, October 24, 2019
By Sara Harrison
“There have been previous attempts to use machine learning to detect patterns that make one molecule smell like garlic and another like jasmine… Several other teams applied AI to that data and made successful predictions. But Wiltschko’s team took a different approach. They used something called a graph neural network, or GNN.”
Google has its own perfume—or at least one team of the company’s researchers does. Crafted under the guidance of expert French perfumers, the mixture has notes of vanilla, jasmine, melon, and strawberries. “It wasn’t half bad,” says Alex Wiltschko, who keeps a vial of the perfume in his kitchen.
Google’s not marketing that scent anytime soon, but it is sticking its nose into yet another aspect of our lives: smell. On Thursday, researchers at Google Brain released a paper on the preprint site Arxiv showing how they trained a set of machine-learning algorithms to predict molecules’ smell based on their structures. Is this as useful as providing maps for most of the world? Maybe not. But for the field of olfaction, it could help puzzle out some big and long-standing questions.
The science of smell lags behind many other fields. Light, for example, has been understood for centuries. In the 17th century, Isaac Newton used prisms to divide the white light of the sun into our now familiar red, orange, yellow, green, blue, indigo, and violet rainbow. Subsequent research revealed that what we perceive as different colors are actually different wavelengths. Glance at a color wheel and you get a simple representation of how those wavelengths compare, the longer reds and yellows transitioning into the shorter blues and purples. But smell has no such guide.
If wavelengths are the basic components of light, molecules are the building blocks of scents. When they get into our noses, those molecules interact with receptors that send signals to a small part of our brains called the olfactory bulb. Suddenly we think “mmm, popcorn!” Scientists can look at a wavelength and know what color it will look like, but they can’t do the same for molecules and smell.
In fact, it’s proven extremely difficult to figure out a molecule’s odor from its chemical structure. Change or remove one atom or bond, “and you can go from roses to rotten eggs,” says Wiltschko, who led the Google research team for the project.
There have been previous attempts to use machine learning to detect patterns that make one molecule smell like garlic and another like jasmine. Researchers created a DREAM Olfaction Prediction Challenge in 2015. The project crowdsourced scent descriptions from hundreds of people, and researchers tested different machine-learning algorithms to see if they could train them to predict how the molecules smell.
Several other teams applied AI to that data and made successful predictions. But Wiltschko’s team took a different approach. They used something called a graph neural network, or GNN.
About the Author:
Sara Harrison is a freelancer who covers science and business. She is a graduate of the UC Berkeley School of Journalism and Carleton College.