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Olfaction01:25

Olfaction

44.3K
The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
44.3K
Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

9.2K
The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...
9.2K
Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

8.3K
Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
8.3K
Molecular Models02:00

Molecular Models

38.2K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.2K
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.0K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Related Experiment Video

Updated: Jun 20, 2025

Constructing an Olfactometer for Rodent Olfactory Behavior Studies Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
08:36

Constructing an Olfactometer for Rodent Olfactory Behavior Studies Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: April 11, 2025

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A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics.

Mengji Zhang1,2, Yusuke Hiki3, Akira Funahashi3

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. mengji.zhang0809@gmail.com.

NPJ Systems Biology and Applications
|July 17, 2024
PubMed
Summary
This summary is machine-generated.

Predicting smells from molecules is hard. A new deep learning model, Mol-PECO, uses molecular structure and electrostatics to accurately predict olfactory perceptions, outperforming other methods.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Machine learning

Background:

  • Olfactory perception prediction from molecular structures is complex due to the discontinuous nature of smell perception.
  • Existing methods often struggle to capture the nuances of odorant-receptor interactions.

Purpose of the Study:

  • To introduce Mol-PECO, a deep learning model for predicting olfactory perceptions from molecular structures and electrostatics.
  • To demonstrate Mol-PECO's superiority over traditional machine learning and graph neural network approaches.

Main Methods:

  • Developed Mol-PECO, a deep learning model utilizing the Coulomb matrix for molecular representation and positional encoding.
  • Trained and evaluated Mol-PECO on a comprehensive dataset of odor molecules and their descriptors.
  • Compared Mol-PECO's performance against traditional machine learning methods and graph neural networks.

Main Results:

  • Mol-PECO significantly outperforms traditional machine learning methods and graph neural networks in predicting olfactory perceptions.
  • The learned molecular embeddings by Mol-PECO effectively capture the odor space.
  • Mol-PECO enables global clustering of odor descriptors and local retrieval of similar odorant molecules.

Conclusions:

  • Mol-PECO provides an effective deep learning framework for predicting olfactory perceptions.
  • The Coulomb matrix offers a powerful alternative for molecular representation in olfactory prediction tasks.
  • This research advances the understanding of olfactory mechanisms and molecular interactions.