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Learning to Smell: Using Deep Learning to Predict the Olfactory Properties of Molecules

Thursday, October 24, 2019

Posted by Alexander B Wiltschko, Senior Research Scientist, Google Researchtrigger vivid memoriesolfactory receptorsolfactory sensory neuronsolfactory epitheliumolfactory bulbVanillinsweet, vanilla, creamy,chocolatemulti-label classificationMachine Learning for Scent: Learning Generalizable Perceptual Representations of Small MoleculesgraphsGraph Neural Networks for Odor Predictionnatural model of choicemessage passing

Each node is represented as a vector, and each entry in the vector initially encodes some atomic-level information.

For each node we look at adjacent nodes and collect their information, which is then transformed with a neural network into new information for the centered node. This procedure is performed iteratively. Other variants of GNNs utilize edge and graph-level information.

Illustration of a GNN for odor prediction. We translate the structure of molecules into graphs that are fed into GNN layers to learn a better representation of the nodes. These nodes are reduced into a single vector and passed into a neural network that is used to predict multiple odor descriptors.

stereoisomers(R)- and (S)-carvonerandom forests

Example of the performance of a GNN on odor descriptors against a strong baseline, as measured by the AUROC score. Example odor descriptors are picked randomly. Closer to 1.0 means better. In the majority of cases GNNs outperform the field-standard baseline substantially, with similar performance seen against other metrics (e.g., AUPRC, recall, precision).

Learning from the Model, and Extending It to Other Taskscolor spacecommon chemoinformatic representation

2D snapshot of our embedding space with some example odors highlighted. Left: Each odor is clustered in its own space. Right: The hierarchical nature of the odor descriptor. Shaded and contoured areas are computed with a kernel-density estimate of the embeddings.

Future WorkAcknowledgementsThis early research is the result of the work and advisement of a team of talented researchers and engineers in Google Brain — Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Carey Radebaugh, Max Bileschi, Yoni Halpern, and D. Sculley. We are delighted to have collaborated on this work with Richard Gerkin at ASU and Alán Aspuru-Guzik at the University of Toronto. We are of course building on an enormous amount of prior work, and have benefitted particularly from work by Justin Gilmer, George Dahl and others on fundamental methodology in GNNs, among many other works in neuroscience, statistics and chemistry. We are also grateful to helpful comments from Steven Kearnes, David Belanger, Joel Mainland, and Emily Mayhew.

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