Mapping the physical properties of odorant molecules to their perceptual characteristics.

I pass on parts of the editor's summary and the abstract of a foundational piece of work by Lee et al. that produces a map linking odorant molecular structures to their perceptual experience, analogous to the known maps for vision and hearing that relate physical properties such as frequency and wavelength to perceptual properties such as pitch and color. I also pass on the first few (slightly edited) paragraphs of the paper that set context. Motivated readers can obtain a PDF of the article from me. (This work does not engage the problem, noted by Sagar et al., that the same volatile molecular may smell different to different people - the same odor can smell ‘fruity’ and ‘floral’ to one person and ‘musky’ and ‘decayed’ to another.)  

Summary

For vision and hearing, there are well-developed maps that relate physical properties such as frequency and wavelength to perceptual properties such as pitch and color. The sense of olfaction does not yet have such a map. Using a graph neural network, Lee et al. developed a principal odor map (POM) that faithfully represents known perceptual hierarchies and distances. This map outperforms previously published models to the point that replacing a trained human’s responses with the model output would improve overall panel description. The POM coordinates were able to predict odor intensity and perceptual similarity, even though these perceptual features were not explicitly part of the model training.

Abstract

Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.

Initial paragraphs of text:

A fundamental problem in neuroscience is mapping the physical properties of a stimulus to perceptual characteristics. In vision, wavelength maps to color; in audition, frequency maps to pitch. By contrast, the mapping from chemical structures to olfactory percepts is poorly understood. Detailed and modality-specific maps such as the Commission Internationale de l’Elcairage (CIE) color space (1) and Fourier space (2) led to a better understanding of visual and auditory coding. Similarly, to better understand olfactory coding, the field of olfaction needs a better map.

Pitch increases monotonically with frequency. By contrast, the relationship between odor percept and odorant structure is riddled with discontinuities...frequently structurally similar pairs are not perceptually similar pairs. These discontinuities in the structure-odor relationship suggest that standard chemoinformatic representations of molecules—functional group counts, physical properties, molecular fingerprints, and so on—that have been used in recent odor modeling work are inadequate to map odor space.

To generate odor-relevant representations of molecules, we constructed a message passing neural network (MPNN), which is a specific type of graph neural network (GNN), to map chemical structures to odor percepts. Each molecule was represented as a graph, with each atom described by its valence, degree, hydrogen count, hybridization, formal charge, and atomic number. Each bond was described by its degree, its aromaticity, and whether it is in a ring. Unlike traditional fingerprinting techniques, which assign equal weight to all molecular fragments within a set bond radius, a GNN can optimize fragment weights for odor-specific applications. Neural networks have unlocked predictive modeling breakthroughs in diverse perceptual domains [e.g., natural images, faces, and sounds] and naturally produce intermediate representations of their input data that are functionally high-dimensional, data-driven maps. We used the final layer of the GNN (henceforth, “our model”) to directly predict odor qualities, and the penultimate layer of the model as a principal odor map (POM). The POM (i) faithfully represented known perceptual hierarchies and distances, (ii) extended to out-of-sample (hereafter, “novel”) odorants, (iii) was robust to discontinuities in structure-odor distances, and (iv) generalized to other olfactory tasks.

We curated a reference dataset of ~5000 molecules, each described by multiple odor labels (e.g., creamy, grassy), by combining the Good Scents and Leffingwell & Associates (GS-LF) flavor and fragrance databases. To train our model, we optimized model parameters with a weighted-cross entropy loss over 150 epochs using Adam with a learning rate decaying from 5 × 10−4 to 1 × 10−5 and a batch size of 128. The GS-LF dataset was split 80/20 training/test, and the 80% training set further subdivided into five cross-validation splits. These cross-validation splits were used to optimize hyperparameters using Vizier, a Bayesian optimization algorithm, by tuning across 1000 trials. Details about model architecture and hyperparameters are given in the supplementary methods. When properly hyperparameter-tuned, performance was found to be robust across many model architectures. We present results for the model with the highest mean area under the receiver operating characteristic curve (AUROC) on the cross-validation set (AUROC = 0.89).