Color and pitch perception are largely understandable from characteristics of physical stimuli: the wavelengths of light and sound waves, respectively. By contrast, understanding olfactory percepts from odorous stimuli (volatile molecules) is much more challenging. No intuitive set of molecular features is up to the task. Here in Chemical Senses, the Ray lab reports using a predictive modeling framework—first breaking molecular structure into thousands of features and then using this to train a predictive statistical model on a wide range of perceptual descriptors—to create a tool for predicting the odor character of hundreds of thousands of available but previously uncharacterized molecules (Kowalewski et al. 2021). This will allow future investigators to representatively sample the space of odorous molecules as well as identify previously unknown odorants with a target odor character. Here, I put this work into the context of other modeling efforts and highlight the urgent need for large new datasets and transparent benchmarks for the field to make and evaluate modeling breakthroughs, respectively.

中文翻译：

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及时解析鼠尾草和迷迭香：破解嗅觉感知的机器学习竞赛

颜色和音高感知在很大程度上可以从物理刺激的特征中理解：分别是光波和声波的波长。相比之下，从气味刺激（挥发性分子）中理解嗅觉感知更具挑战性。没有一组直观的分子特征可以胜任这项任务。在 Chemical Senses 中，Ray 实验室报告说使用了预测建模框架——首先将分子结构分解为数千个特征，然后使用它在广泛的感知描述符上训练预测统计模型——以创建用于预测气味特征的工具数十万个可用但以前未表征的分子（Kowalewski et al. 2021）。这将使未来的研究人员能够代表性地对气味分子的空间进行采样，并识别具有目标气味特征的以前未知的气味剂。在这里，我将这项工作置于其他建模工作的背景下，并强调迫切需要大型新数据集和该领域的透明基准，以分别取得和评估建模突破。