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Predicting Human Olfactory Perception from Molecular Structure

从分子结构预测人类嗅觉

基本信息

批准号：
10249061
负责人：
Joel D Mainland
金额：
$ 43.74万
依托单位：
MONELL CHEMICAL SENSES CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10249061
关键词：
Algorithms Characteristics Chemical Structure Chemicals Code Collection Color Communities Complex Complex Mixtures Country Data Data Set Descriptor Detection Development Eating Enrollment Face Food Food Preferences Frequencies Health Human Ligands Machine Learning Mass Fragmentography Measurement Measures Methods Modeling Modernization Molecular Structure Neurosciences Non-linear Models Numerical value Odors Olfactory Pathways Perception Play Process Psychophysics Research Research Personnel Role Smell Perception Stimulus Techniques Technology Training Translating Vision Visual Vocabulary Work auditory stimulus base computer monitor disease diagnosis experience high dimensionality improved in silico interest machine learning algorithm member novel physical property predictive modeling predictive test preference receptor relating to nervous system single molecule visual stimulus

项目摘要

PROJECT SUMMARY Modern technology makes it possible to capture a visual scene as a photograph, alter it, send it to another country nearly instantaneously, and store it without concern for degradation. None of this is currently possible in olfaction. Although perfumers and flavorists are adept at mixing odorous molecules to produce a desired perceptual effect, the rules underlying this process are poorly understood at a quantitative level. Current methods for displaying odors to a subject are akin to requiring a Polaroid of every visual stimulus of interest. A more efficient method for probing the olfactory system would be to use a set of 'primary odors'—some limited number of odors from which all other complex odors could be reproduced by appropriate mixtures. Both auditory and visual stimuli have been digitized, and this will eventually be possible in olfaction as well. Predicting odor from chemical structure has been a problem in the field since its inception, but recent advances in machine learning algorithms have made great progress in analogous problems, such as facial recognition. The research proposed here will combine these machine learning techniques with high quality human psychophysics to understand how to predict the smell of a molecule or mixture of odorants, which will ultimately help improve our understanding of disease diagnosis using odors as well as eating-related health and illness.

项目摘要现代技术使人们有可能捕捉一个视觉场景作为一张照片，改变它，把它发送到另一个国家几乎瞬间，并存储它，而不必担心退化。这些目前都不可能实现在嗅觉方面。尽管调香师和调味师擅长混合气味分子以产生所需的香味，虽然这是一种感性的影响，但在定量层面上对这一过程的基本规则了解甚少。电流用于向受试者显示气味的方法类似于需要感兴趣的每个视觉刺激的宝丽来。一探测嗅觉系统的更有效的方法是使用一组“主要气味”--一些有限的通过适当的混合物可以再现所有其他复杂气味的气味数量。两听觉和视觉刺激已经被数字化，这最终也将在嗅觉中成为可能。从化学结构预测气味从一开始就一直是该领域的一个问题，但最近的进展在机器学习中，算法在类似的问题上取得了很大的进展，例如面部识别。本文提出的研究将联合收割机这些机器学习技术与高质量的人类心理物理学，以了解如何预测分子或气味混合物的气味，这将最终有助于提高我们对疾病诊断的理解，和疾病