Researchers at artificial intelligence (AI) company Osmo, working with colleagues at Philadelphia’s Drexel University and the Monell Chemical Senses Center, developed a graph neural network that reliably matched human volunteers' identification of 55 odors, then predicted the smells of 500,000 additional molecules without having to produce or sniff them.
The researchers fed the structures and odor descriptions of 5,000 molecules to an AI to teach it to identify patterns in the training data by correlating a molecule's odor with attributes of its underlying atoms.
After calculating average human odor identification ratings, the researchers found the neural network got closer to this average than any individual in the volunteer group did in over half the cases.
The AI then deduced how the 500,000 hypothetical chemical structures should smell.
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