| Title: | Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception |
| Address: | "School of Life Sciences, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA" |
| ISSN/ISBN: | 1464-3553 (Electronic) 0379-864X (Print) 0379-864X (Linking) |
| 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" |
| Keywords: | Humans *Machine Learning Molecular Structure *Odorants Olfactory Perception/*physiology Time Factors Volatile Organic Compounds/*chemistry computation feature extraction modeling olfaction psychophysics smell; |
| Notes: | "MedlineGerkin, Richard C eng R01 DC018455/DC/NIDCD NIH HHS/ U01 DC019573/DC/NIDCD NIH HHS/ U19 NS112953/NS/NINDS NIH HHS/ R01 DC017757/DC/NIDCD NIH HHS/ Research Support, N.I.H., Extramural England 2021/04/17 Chem Senses. 2021 Jan 1; 46:bjab020. doi: 10.1093/chemse/bjab020" |
