Stworzono sieć neuronową umożliwiającą mapowanie zapachów na podstawie cząsteczek [ENG]

Sensory sciences have come a long way in explaining how some physical phenomena—a particular wavelength of light, for example, or a column of air vibrating at a set frequency—correspond to a typical perceptual experience. The sense of smell, however, has proven elusive. Until recently, there was simply no way to take the physical properties of a compound or the structural formula of a molecule and have any sense of what it might smell like.

Using a type of deep-learning algorithm called a graph neural network, researchers have built a model that maps chemical structure to odor descriptors. The model has successfully predicted how a panel of humans would describe new smells, and it could be an important step along a long path toward digitizing smells. The work is described in a study published 31 August in Science.

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