The company said identifying smell is a multi-label classification problem, meaning a substance can have multiple smell characteristics. For instance, Vanillin, a substance often used to create an artificial vanilla flavor, has multiple smell descriptors such as sweet, vanilla, and chocolate, with some characteristics stronger than others. So, to identify the smell profile of a molecule researchers used a graph neural networks (GNNs), a deep learning model that takes graphs as inputs. The team took the help of perfume experts to create labels of smell that can be used to identify a molecule’s olfactory properties. The neural network starts the process by creating a representative vector using various properties such as atom identity and atom charge. Then it broadcasts the vector to a neighboring node, and then collectively passes to update function to get a vector for centered node. This process is repeated for a layer, and then it continues for multiple layers. Finally, the AI sums up or averages a vector for a molecule to identify multiple olfactory identifiers. Google researchers said not only this model outperforms older methods, but it can be used to predict new or unclassified smells in RGB-layout like “odor embedding”. In the future, the team wants to create solutions for digitalized scent creations and even build solutions for those without a sense of smell. Plus, it wants to create more open datasets for research so researchers can leverage them for various scent-related machine learning models.
