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Researchers have long known that the chemical structure of the molecules we inhale influences what we smell. But in most cases, no one can figure out exactly how. Scientists have deciphered a few specific rules that govern how the nose and brain perceive an airborne molecule based on its characteristics. It has become clear that we quickly recognize some sulfur-containing compounds as the scent of garlic, for example, and certain ammonia-derived amines as a fishy odor. But these are exceptions.

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研究人员早已知道,我们吸入的分子的化学结构影响我们闻到的气味。但在大多数情况下,没有人能够弄清楚其中的机制。科学家们已经破译了一系列具体的规则,这些规则负责控制鼻子和大脑根据空气中的分子的特性感知气味。已经清楚的事实是,我们可以很快识别出一些含硫化合物的气味,例如有的含硫化合物就像大蒜一样,而某些氨衍生胺类物质有鱼腥味。但这些都是例外。

It turns out that structurally unrelated molecules can have similar scents. For example, hydrogen cyanide and larger, ring-shaped benzaldehyde both smell like almonds. Meanwhile tiny structural changes—even shifting the location of one double bond—can dramatically alter a scent.

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事实证明,结构不相关的分子也会有相似的气味。例如,氰化氢和更大的环状苯甲醛闻起来都像杏仁。同时,细小的结构变化——即使只改变一个双键的位置——都能极大地改变气味。

To make sense of this baffling chemistry, researchers have turned to the computational might of artificial intelligence. Now one team has trained a type of AI known as a graph neural network to predict what a compound will smell like to a person—rose, medicinal, earthy, and so on—based on the chemical features of odor molecules. The computer model assessed new scents as reliably as humans, the researchers report in a new draft paper posted to the preprint repository bioRxiv.

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为了理解这种令人困惑的化学现象,研究人员转而借助于人工智能强大的计算能力。现在,一个团队训练了一种名为图形神经网络的人工智能,基于气味分子的化学特性来预测化合物对人体来说闻起来的气味,玫瑰味、药味、泥土味等等。研究人员在发布在预印本存储库bioRxiv上的新论文草稿中报告称,计算机模型对新气味的评估和人类感觉到的一样可靠。

Stuart Firestein, an olfactory neuroscientist at Columbia University, calls the model a “tour de force work of computational biology.” But as is typical for a lot of machine-learning-based studies, “it never gives you, in my opinion, much of a deeper sense of how things are working,” says Firestein, who was not involved in the paper. His critique stems from an inherent feature in the technology: such neural networks are generally not interpretable, meaning human researchers cannot get access to the reasoning a model uses to solve a problem.

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哥伦比亚大学的嗅觉神经科学家斯图尔特·费尔斯坦把该模型称之为“计算机生物学的杰作”。但就像很多机器学习研究的典型问题一样,“在我看来,它从未让你对事物的作用机理有更深层次的理解,”未参与本研究的菲尔坦斯说。

What’s more, this model skips over the inscrutable workings of the nervous system, instead making direct connections between molecules and smells. Even so, Firestein and others describe it as a potentially useful tool with which to study the sense of smell and its fraught relationship with chemistry. To the researchers involved, the model also represents a move toward a more precise, numbers-based means to describe the odor universe, which they hope could eventually bring this sense to the digital world.

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更重要的是,这个模型跳过了神经系统难以捉摸的运作方式,而是直接在分子和气味之间建立联系。即便如此,费尔斯坦和其他人认为其在研究嗅觉及其与化学之间的复杂关系上,仍是一个可能有作用的工具。对相关研究人员来说,该模型也代表着向更精确的、基于数字的方法来描述气味世界的迈进,他们希望这最终能将这种感觉带到数字世界中。

本文节选自:Scientific American

发布时间:2022.10.27

作者:Wynne Parry

原文标题:AI Predicts What Chemicals Will Smell like to a Human

◆词汇积累

1.decipher

英/ dɪˈsaɪfə(r) /美/ dɪˈsaɪfər /v.破译,辨认(难认、难解的东西);理解(神秘或难懂的事物)

2.amine

英/ əˈmiːn /美/ əˈmin /n.[有化]胺(等于 amin)n.(Amine)人名;(阿拉伯)阿明;(俄)阿米涅

3.almond

英/ ˈɑːmənd /美/ ˈɑːmənd /n.杏仁, 扁桃仁;杏树,扁桃树【名】 (Almond)(英)阿尔蒙德,(法)阿尔蒙(人名)

4.baffle

英/ ˈbæf(ə)l /美/ ˈbæf(ə)l /v.使困惑,难住;抑制,控制n.挡板,隔板,反射板

◆词组搭配

1.skip over 略过;遗漏;作短期旅行

2.even so 虽然如此,即使如此

◆写作句总结

原句:the model also represents a move toward a more precise, numbers-based means to describe the odor universe, which they hope could eventually bring this sense to the digital world.

结构:XX represents a move toward a more XX thing to do sth, which they hope could eventually XX.

例句:The approach also represents a move towards more efficient, energy-efficient ways to operate in the industry, which they hope will eventually lead to industry-wide applications.