Researchers at the University of California, Riverside, have used machine learning to understand what a chemical smells like -- a U.S. National Science Foundation-funded research breakthrough with potential applications in the food, flavor and fragrance industries.
"We now can use artificial intelligence to predict how any chemical is going to smell to humans," said Anandasankar Ray, a molecular biologist and senior author of a study that appears in iScience. "Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals."
Humans sense odors when some of their nearly 400 odorant receptors, or ORs, are activated in the nose. Each OR is activated by a unique set of chemicals; together, the large OR family can detect a vast chemical space. A key question in olfaction is how the receptors contribute to different perceptual qualities, or percepts.
"We tried to model human olfactory percepts using chemical informatics and machine learning," Ray said. "The power of machine learning is that it is able to evaluate a large number of chemical features and learn what makes a chemical smell like, say, a lemon or a rose or something else. The machine learning algorithm can eventually predict how a new chemical will smell even though we may initially not know if it smells like a lemon or a rose."
According to Ray, digitizing predictions of how chemicals smell creates a new way of scientifically prioritizing what chemicals can be used in the food, flavor, and fragrance industries.
"It allows us to rapidly find chemicals that have a novel combination of smells," he said. "The technology can help us discover new chemicals that could replace existing ones that are becoming rare, for example, or are very expensive. It gives us a vast palette of compounds that we can mix and match for any olfactory application. For example, you can now make a mosquito repellent that works on mosquitoes but is pleasant smelling to humans."