Is It Possible to Predict the Odor of a Molecule on the Basis of its Structure?

• Published in: International journal of molecular sciences Vol. 20; no. 12; p. 3018
• Main Authors: Genva, Manon, Kenne Kemene, Tierry, Deleu, Magali, Lins, Laurence, Fauconnier, Marie-Laure
• Format: Journal Article Web Resource
• Language: English
• Published: Switzerland MDPI AG 20.06.2019; Multidisciplinary Digital Publishing Institute (MDPI); MDPI
• Subjects: Animals; Axons; Bathing; Biochemistry, biophysics & molecular biology; Biochimie, biophysique & biologie moléculaire; Carbon; Chemosensory perception; Cilia; Electronic Nose; Epithelium; flavor; Humans; Life sciences; Machine Learning; Mitral cells; Molecular structure; Mucus; Musk; Nose; odor; Odor intensity; odorant; Odorant receptors; Odorants - analysis; Odors; olfaction; Olfactory Perception; olfactory sense; Olfactory system; Organic compounds; Physiology; Receptors, Odorant - metabolism; Review; Sciences du vivant; sensory perception; Smell; structure-function relationship; volatile organic compounds; flavor; sensory perception; volatile organic compounds; odorant; structure-function relationship; olfaction; odor; olfactory sense
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms20123018

The olfactory sense is the dominant sensory perception for many animals. When Richard Axel and Linda B. Buck received the Nobel Prize in 2004 for discovering the G protein-coupled receptors' role in olfactory cells, they highlighted the importance of olfaction to the scientific community. Several theories have tried to explain how cells are able to distinguish such a wide variety of odorant molecules in a complex context in which enantiomers can result in completely different perceptions and structurally different molecules. Moreover, sex, age, cultural origin, and individual differences contribute to odor perception variations that complicate the picture. In this article, recent advances in olfaction theory are presented, and future trends in human olfaction such as structure-based odor prediction and artificial sniffing are discussed at the frontiers of chemistry, physiology, neurobiology, and machine learning.