A Structure-Based Approach for Predicting Odor Similarity of Molecules via Docking Simulations with Human Olfactory Receptors

The mechanisms underlying human odor recognition remain largely unclear, making it challenging to predict the scent of a novel molecule based solely on its molecular structure. Unlike taste, which is classified into a limited number of categories, odor perception is highly complex and lacks universally defined labels, rendering absolute odor classification inherently ambiguous. To address this issue, we propose a relative evaluation framework for odor prediction, focusing on odor similarity rather than absolute descriptors. In this study, we constructed three-dimensional structures of approximately 400 human olfactory receptors (hORs) using AlphaFold2 and performed molecular docking simulations with odorant compounds. Each odorant was represented as a 409-dimensional docking score vector, and odor similarity was inferred by comparing these vectors statistically. To evaluate the effectiveness of this approach, we used odorant molecules from the ATLAS database and tested whether molecules with similar docking profiles correspond to similar olfactory perceptions. Our results demonstrate that the proposed docking-based method enables the relative prediction of odor similarity between molecules, even for compounds not included in the reference database. This method offers a promising alternative to traditional QSAR-based approaches relying solely on structural similarity, and provides a structure-based, receptor-level framework for computational olfaction.