Using artificial intelligence to smell the roses
Study applies machine learning to olfaction with possible vast
applications in flavors and fragrances

Date:
July 28, 2020
Source:
University of California - Riverside
Summary:
A pair of researchers has used machine learning to understand what
a chemical smells like -- a research breakthrough with potential
applications in the food flavor and fragrance industries.



FULL STORY ==========================================================================
A pair of researchers at the University of California, Riverside,
has used machine learning to understand what a chemical smells like --
a 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 professor of
molecular, cell and systems biology, and the senior author of the 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 which 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." The researchers first developed a method
for a computer to learn chemical features that activate known human
odorant receptors. They then screened roughly half a million compounds
for new ligands -- molecules that bind to receptors -- for 34 odorant receptors. Next, they focused on whether the algorithm that could estimate odorant receptor activity could also predict diverse perceptual qualities
of odorants.



========================================================================== "Computers might help us better understand human perceptual coding, which appears, in part, to be based on combinations of differently activated
ORs," said Joel Kowalewski, a student in the Neuroscience Graduate
Program working with Ray and the first author of the research paper. "We
used hundreds of chemicals that human volunteers previously evaluated,
selected ORs that best predicted percepts on a portion of chemicals,
and tested that these ORs were also predictive of new chemicals."
Ray and Kowalewski showed the activity of ORs successfully predicted 146 different percepts of chemicals. To their surprise, few rather than all
ORs were needed to predict some of these percepts. Since they could not
record activity from sensory neurons in humans, they tested this further
in the fruit fly (Drosophila melanogaster) and observed a similar result
when predicting the fly's attraction or aversion to different odorants.

"If predictions are successful with less information, the task of decoding
odor perception would then become easier for a computer," Kowalewski said.

Ray explained that many items available to consumers use volatile
chemicals to make themselves appealing. About 80% of what is considered
flavor in food actually stems from the odors that affect smell. Fragrances
for perfuming cosmetics, cleaning products, and other household goods
play an important role in consumer behavior.

"Our digital approach using machine learning could open up many
opportunities in the food, flavor, and fragrance industries," he said. "We
now have an unprecedented ability to find ligands and new flavors and fragrances. Using our computational approach, we can intelligently design volatile chemicals that smell desirable for use and also predict ligands
for the 34 human ORs." The study was partially funded by UCR and the
National Science Foundation.


========================================================================== Story Source: Materials provided by
University_of_California_-_Riverside. Original written by Iqbal
Pittalwala. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Joel Kowalewski, Anandasankar Ray. Predicting Human Olfactory
Perception
from Activities of Odorant Receptors. iScience, 2020; 23 (8):
101361 DOI: 10.1016/j.isci.2020.101361 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200728182544.htm

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