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September 10, 2025

Interpretable Message‐Passing Neural Networks for Molecular Odour Perception Prediction: From Structural Topology to Olfactory Characterisation

Puntos clave

The MPNN-based model achieves robust predictive performance across various odour dimensions, indicating its effectiveness in flavour characterization.
Results showed an F1-score greater than 0.73 and AUC above 0.82 for key odour categories, showcasing strong predictive accuracy.
Mechanistic analysis explains how specific functional groups relate to odour perception, offering insights for modifying flavours.
This novel approach integrates classification with feature visualisation, enhancing interpretability beyond traditional QSAR models.

Resumen

ABSTRACT Molecular odour, as a crucial chemical property, serves as the foundation for flavour modulation in food product development, directly impacting applications ranging from precision design of food flavours and fragrance synthesis to environmental monitoring. However, the structure–activity relationship between molecular characteristics and olfactory perception remains incompletely understood, while traditional experimental approaches face challenges in large‐scale molecular odour characterisation. Recent advances have highlighted the growing importance of artificial intelligence algorithms integrated with chemical data processing techniques for molecular property prediction and rational interpretation of structure‐flavour correlations. This study develops a MPNN‐based fusion model that constructs a multi‐label binary classification framework by integrating molecular topological features with chemical feature maps. Through atomic‐level feature embedding and bond relationship propagation mechanisms, our architecture effectively captures cross‐scale structural patterns from a meticulously curated molecular odour dataset. The model demonstrates robust predictive performance across key food‐related odour dimensions including Odourless, Fruity, Offensive, Oily and Woody (F1‐score: > 0.73, AUC: > 0.82), while maintaining strong generalisation capabilities for low‐abundance labels (e.g., Alcoholic, n = 50: F1‐score = 0.6940, AUC: = 0.8612). The mechanistic analysis revealed that gradient backpropagation can elucidate quantitative associations between specific functional groups and odour perception, providing actionable insights for rational modification of flavour molecules. Our work innovatively integrates multi‐label classification with feature visualisation, overcoming the interpretability limitations inherent in conventional QSAR models for odour prediction. The developed computational framework provides an extensible technical pathway for intelligent fragrance development and odour digitisation engineering, establishing a novel algorithmic paradigm for next‐generation food sensory innovation.

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