Interpretable multitask deep learning models for odor perception based on molecular structure.

Understanding the relationship between molecular structure and odor perception is essential for applications in fragrance formulation, food product development, and pharmaceutical design. Traditional approaches often rely on sensory evaluations or expert-engineered molecular features, which are labor-intensive and lack scalability. In this study, we developed a multitask learning model capable of simultaneously predicting multiple odor categories from chemical structures, aiming to capture shared representations across related odors. Using a graph neural network-based architecture (kMoL) trained on experimental data spanning 14 odor categories, the proposed model outperformed conventional single-task models and Random Forests in both accuracy and stability. Label co-occurrence analysis revealed that compounds frequently exhibit multiple odor characteristics, and a higher degree of label overlap was associated with improved performance in the multitask setting. Chemical structure visualization using UMAP and t-SNE showed no pronounced clustering by odor type, suggesting a balanced prediction difficulty across categories. To enhance interpretability, we applied Integrated Gradients to identify atom-level contributions, which aligned with known substructures relevant to olfactory receptor interactions, including hydrogen-bond donors and aromatic rings. Notably, for sweet-smelling compounds such as maltol, our model highlighted regions that correspond to interaction sites identified in receptor-ligand docking studies. These findings demonstrate that the multitask model not only delivers strong predictive performance but also captures chemically and biologically relevant features. This approach supports a mechanistic understanding of structure-odor relationships and provides a scalable, interpretable framework for rational olfactory design.