Follow us twitter facebook
Edition: Global
Click here to subscribe toour free weekly newsletter click here
Science, R&D

Google Research team used machine learning to train AI to recognize smells

Last week, a Google Research team published a paper about how they trained an artificial intelligence (AI) using machine learning to reasonably predict “the relationship between a molecule’s structure and its odor,” aka recognize different smells based on molecule shapes.

Predicting the relationship between a molecule’s structure and its odor remains a difficult. This problem, termed quantitative structure-odor relationship (QSOR) modeling, is an important challenge in chemistry, with a strong potential impact on the manufacture of synthetic fragrance, among others.

A team of Google researchers recently published a paper on how they trained a neural network to connect a molecule’s structure with its odor. (Photo: © grinvalds/Istock.com)

A team of Google researchers recently published a paper on how they trained a neural network to connect a molecule’s structure with its odor. (Photo: © grinvalds/Istock.com)

A paper published on October 23, 2019, by a team of Google researchers tackles how machine learning can be used to find the relationship - which scientists have been trying to quantify for over 70 years - between a molecule’s odor and its structure.

While molecular structures can give scientists insight as to what something looks like or what it sounds like, “predicting the relationship between a molecule’s structure and its odor remains a difficult, decades-old task.

These scientists used machine learning to find this underlying quantitative relationship in a way reminiscent of how deep learning is used to predict visual and auditory characteristics.

Using a dataset of 5030 molecules labeled with odor descriptors (like fruity, bready, nutty and cheese) by olfactory experts (like professional perfumers), the team trained a Graph Neural Network to predict these labels based on the molecule’s shape. The perceptual and structural similarities of both local and global components of a molecule aided this neural network in recognizing which parts are responsible for which fragrance nodes.

According to the paper, being able to use machine learning to predict odors based on their molecular structures would “aid in the discovery of new synthetic odorants” so that natural products don’t need to be harvested in such high numbers.

The latest trends and innovations in fragrance will be presented on November 7, 2019 at the Fragrance Innovation Summit in Paris.

Program and registration: www.fragranceinnovation.com

Premium Beauty News with AFP/Relaxnews

© 2019 - Premium Beauty News - www.premiumbeautynews.com
latest news
Focus
spip-vignette

A new trade show dedicated to clean beauty in London

Dubbed Clean Beauty in London the new trade show intends to gather experts, scientists, suppliers, brands, influencers and journalists under one roof “to build the future of clean beauty.” Taking place on October 12 & 13, 2020 at The Brewery, located at 52 Chiswell Street at the heart of London, the event aims to encourage (...)

read more
job opportunities
Experts’ views
Haitian crisis: The Vetiver sector under great tension

Remi Pulverail
Haitian crisis: The Vetiver sector under great tension

What about this country that has been struggling for years between natural disasters, less and less efficient politicians, poorly managed and inefficient international aid and the consequences for the fragrance industry? Without going back to the times of colonization, Haiti has suffered in recent years from several phenomena that (...)

read more

Features

We use cookies to give you a better browsing experience. By continuing your visit to this site, you accept the use of cookies. Read more and set cookies
close