White John, Graf Johnathan, Haines Samuel, Sathitsuksanoh Noppadon, Eric Berson R, Jaeger Vance W
Chemical Engineering Department, University of Louisville, 216 Eastern Pkwy, Louisville, KY, 40208, USA.
Chempluschem. 2025 Jan;90(1):e202400459. doi: 10.1002/cplu.202400459. Epub 2024 Nov 1.
Henry's law describes the vapor-liquid equilibrium for dilute gases dissolved in a liquid solvent phase. Descriptions of vapor-liquid equilibrium allow the design of improved separations in the food and beverage industry. The consumer experience of taste and odor are greatly affected by the liquid and vapor phase behavior of organic compounds. This study presents a machine learning (ML) based model that allows quick, accurate predictions of Henry's law constants (k) for many common organic compounds. Users input only a Simplified Molecular-Input Line-Entry System (SMILES) string or a common English name, and the model returns Henry's law estimates for compounds in water and ethanol. Training was performed on 5,690 compounds. Training data were gathered from an existing database and were supplemented with quantum mechanical (QM) calculations. An extra trees regression model was generated that predicts k with a mean absolute error of 1.3 in log space and an R of 0.98. The model is applied to common flavor and odor compounds in bourbon whiskey as a test case for food and beverage applications.
亨利定律描述了溶解在液态溶剂相中的稀气体的气液平衡。气液平衡的描述有助于食品和饮料行业改进分离工艺的设计。有机化合物的液相和气相行为对消费者的味觉和嗅觉体验有很大影响。本研究提出了一种基于机器学习（ML）的模型，该模型能够快速、准确地预测许多常见有机化合物的亨利定律常数（k）。用户只需输入简化分子输入线性条目系统（SMILES）字符串或通用英文名，该模型就能返回化合物在水和乙醇中的亨利定律估计值。对5690种化合物进行了训练。训练数据来自现有数据库，并辅以量子力学（QM）计算。生成了一个额外树回归模型，该模型在对数空间中预测k的平均绝对误差为1.3，R为0.98。该模型应用于波旁威士忌中的常见风味和气味化合物，作为食品和饮料应用的测试案例。
