Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components

Abstract

Predicting olfactory perception directly from molecular structure is central to fragrance design that plays a role in a wide range of industries, such as perfumery, food and beverage, and health care. Among olfactory attributes, odor strength is a key factor in shaping odor perception, but its modeling has been impeded by scarce and fragmented intensity data. In this work, we introduce an ordinal odor strength data set of over 2,000 molecules by integrating two different public sources, mapping structures to odorless, low, medium, and high categories. Across several molecular encodings and supervised learning algorithms we compared different prediction strategies. Dimensionality reduction and SHAP analysis identifies molecular size, polarity, ring features, and branching as primary drivers, consistent with mass-transport constraints on volatility, sorption, and receptor access. This scalable ordinal framework enables reliable odor-strength estimation for novel molecules and provides a foundation for in silico fragrance design.

26 pages, 25 figures

References in corpus (6)

• XGBoost: A Scalable Tree Boosting System
• Rank consistent ordinal regression for neural networks with application to age estimation
• ChemBERTa-2: Towards Chemical Foundation Models
• A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them
• Deep Learning Foundation Models from Classical Molecular Descriptors
• From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases