Researchers at the University of California, Riverside (UCR) used machine learning to help a computer understand what a chemical smells like to humans.
UCR's Anandasankar Ray and Joel Kowalewski developed a technique for a computer to learn chemical features that activate known human odorant receptors (ORs), then screened about 500,000 compounds for binding ligand molecules for 34 ORs.
The team then concentrated on whether their OR activity-estimating algorithm could predict diverse perceptual qualities of odorants, and learned that such activity accurately predicted 146 distinct chemical percepts.
Only a few ORs were required for prediction.
Said Ray, "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."
From UC Riverside News
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