Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

Color and pitch perception are largely understandable from characteristics of physical stimuli: the wavelengths of light and sound waves, respectively. By contrast, understanding olfactory percepts from odorous stimuli (volatile molecules) is much more challenging. No intuitive set of molecular features is up to the task. Here in Chemical Senses, the Ray lab reports using a predictive modeling framework - first breaking molecular structure into thousands of features and then using this to train a predictive statistical model on a wide range of perceptual descriptors - to create a tool for predicting the odor character of hundreds of thousands of available but previously uncharacterized molecules (Kowalewski et al. 2021). This will allow future investigators to representatively sample the space of odorous molecules as well as identify previously unknown odorants with a target odor character. Here, I put this work into the context of other modeling efforts and highlight the urgent need for large new datasets and transparent benchmarks for the field to make and evaluate modeling breakthroughs, respectively.

Original languageEnglish (US)
Article numberbjab020
JournalChemical Senses
Volume46
DOIs
StatePublished - 2021

Keywords

  • computation
  • feature extraction
  • modeling
  • olfaction
  • psychophysics
  • smell

ASJC Scopus subject areas

  • Physiology
  • Sensory Systems
  • Physiology (medical)
  • Behavioral Neuroscience

Fingerprint

Dive into the research topics of 'Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception'. Together they form a unique fingerprint.

Cite this