Interdepartmental Neuroscience Program, University of California, 3401 Watkins Drive, Riverside, CA 92521, USA.
Department of Molecular, Cell and Systems Biology, University of California, 3401 Watkins Drive, Riverside, CA 92521, USA.
Chem Senses. 2021 Jan 1;46. doi: 10.1093/chemse/bjab007.
The fundamental units of olfactory perception are discrete 3D structures of volatile chemicals that each interact with specific subsets of a very large family of hundreds of odorant receptor proteins, in turn activating complex neural circuitry and posing a challenge to understand. We have applied computational approaches to analyze olfactory perceptual space from the perspective of odorant chemical features. We identify physicochemical features associated with ~150 different perceptual descriptors and develop machine-learning models. Validation of predictions shows a high success rate for test set chemicals within a study, as well as across studies more than 30 years apart in time. Due to the high success rates, we are able to map ~150 percepts onto a chemical space of nearly 0.5 million compounds, predicting numerous percept-structure combinations. The chemical structure-to-percept prediction provides a system-level view of human olfaction and opens the door for comprehensive computational discovery of fragrances and flavors.
嗅觉感知的基本单位是离散的 3D 结构挥发性化学物质，每个化学物质都与数百种气味受体蛋白的特定亚群相互作用，进而激活复杂的神经回路，这给我们的理解带来了挑战。我们已经应用计算方法从气味化学特征的角度来分析嗅觉感知空间。我们确定了与约 150 种不同感知描述符相关的物理化学特征，并开发了机器学习模型。对预测结果的验证表明，在一项研究内的测试集化学物质以及跨越 30 多年时间的多项研究中的预测成功率都很高。由于成功率很高，我们能够将约 150 种感觉映射到近 50 万种化合物的化学空间中，预测出许多感知-结构组合。化学结构到感知的预测提供了人类嗅觉的系统级视图，并为全面计算发现香料和风味开辟了道路。
