Molecular Odor Prediction Using Olfactory Receptor Information.

Abstract

In fragrance development, the framework development process is a bottleneck from the perspective of labor, cost, and human resource development. Odors vary greatly depending on the structure and functional groups of the molecule. Although odor has been predicted from only the structure of molecules, its practical application remains elusive. In this study, we developed a model for predicting the odor of molecules that have only small differences in structure. Focusing on the mechanism of human olfaction, we divided the mechanism into three levels and constructed three models: a classification model that predicts the presence or absence of binding between molecules and olfactory receptors, a regression model that predicts the strength of binding, and a classification model that predicts the presence or absence of odor based on the strength of binding. Olfactory receptors were used as descriptors to discriminate between similar molecular odors. Our models predicted odor differences between some similar molecules, including optical isomers.