AI rivals the human nose when it comes to naming smells

When Jonathan Deutsch agreed to sniff 400 vials of unlabeled liquid for science, he didn’t know he would be competing with a computer. A research chef who helps with food product development at Drexel University, he simply welcomed the chance to hone his sense of smell. But odor profiles generated by Deutsch and 13 other volunteers served as a test for a computer program that had been trained to produce these same types of descriptions—such as fruity, cooling, fishy, piny—using chemical structure alone. 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 result is a boon for the study of olfaction, a field that has “floundered around for years looking for this information,” says Stuart Firestein, a neuroscientist at Columbia University who was not involved in the work. “The approach offers great potential” to speed up the search for better smelling consumer products, adds Andreas Grasskamp, a neurobiologist who studies perception at the Fraunhofer Institute for Process Engineering and Packaging. The findings may also help establish olfactory research as a field on par with sight or vision. ...
Source: ScienceNOW - Category: Science Source Type: news