Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous Abstract"Visible-Light-Active N-Doped TiO(2) Photocatalysts: Synthesis from TiOSO(4), Characterization, and Enhancement of Stability Via Surface Modification"    Next Abstract"Studies on floral nectary, tepals' structure, and gynostemium morphology of Epipactis palustris (L.) Crantz (Orchidaceae)" »

Chem Senses


Title:A System-Wide Understanding of the Human Olfactory Percept Chemical Space
Author(s):Kowalewski J; Huynh B; Ray A;
Address:"Interdepartmental Neuroscience Program, University of California, 3401 Watkins Drive, Riverside, CA 92521, USA. Department of Molecular, Cell and Systems Biology, University of California, 3401 Watkins Drive, Riverside, CA 92521, USA"
Journal Title:Chem Senses
Year:2021
Volume:46
Issue:
Page Number: -
DOI: 10.1093/chemse/bjab007
ISSN/ISBN:1464-3553 (Electronic) 0379-864X (Linking)
Abstract:"The fundamental units of olfactory perception are discrete 3D structures of volatile chemicals that each interact with specific subsets of a very large family of hundreds of odorant receptor proteins, in turn activating complex neural circuitry and posing a challenge to understand. We have applied computational approaches to analyze olfactory perceptual space from the perspective of odorant chemical features. We identify physicochemical features associated with ~150 different perceptual descriptors and develop machine-learning models. Validation of predictions shows a high success rate for test set chemicals within a study, as well as across studies more than 30 years apart in time. Due to the high success rates, we are able to map ~150 percepts onto a chemical space of nearly 0.5 million compounds, predicting numerous percept-structure combinations. The chemical structure-to-percept prediction provides a system-level view of human olfaction and opens the door for comprehensive computational discovery of fragrances and flavors"
Keywords:Humans *Machine Learning Molecular Structure *Odorants Olfactory Perception/*physiology Smell/*physiology Software Volatile Organic Compounds/*chemistry flavors fragrances machine learning olfaction prediction;Neuroscience;
Notes:"MedlineKowalewski, Joel Huynh, Brandon Ray, Anandasankar eng England 2021/03/01 Chem Senses. 2021 Jan 1; 46:bjab007. doi: 10.1093/chemse/bjab007"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 17-11-2024