Predicting Human Olfactory Perception from Molecular Structure

项目总结 现代技术使捕捉视觉场景成为可能，将其作为照片进行更改，并将其发送给另一张照片 国家几乎瞬间，并存储它，而不担心退化。这一切目前都不可能实现。 在嗅觉方面。尽管香水师和调味师擅长将气味分子混合在一起，以生产出所需的 知觉效应，这一过程背后的规律在数量层面上知之甚少。当前 向受试者显示气味的方法类似于需要每个感兴趣的视觉刺激的宝丽来。一个 探测嗅觉系统的更有效的方法是使用一组“主要气味”--有些是有限的 所有其他复杂气味可以通过适当的混合物重现的气味数量。两者都有 听觉和视觉刺激已经数字化，这最终也将在嗅觉中实现。 从化学结构预测气味从一开始就是该领域的一个问题，但最近的进展 在机器学习中，算法在人脸识别等类似问题上取得了很大的进展。 本文提出的研究将把这些机器学习技术与高素质的人类结合起来 心理物理学，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利用气味进行疾病诊断以及与饮食相关的健康的理解 和疾病。

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.