Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules.

ABSTRACT: Deciphering the relationship between molecules, olfactory receptors (ORs) and corresponding odors remains a challenging task. It requires a comprehensive identification of ORs responding to a given odorant. With the recent advances in artificial intelligence and the growing research in decoding the human olfactory perception from chemical features of odorant molecules, the applications of advanced machine learning have been revived. In this study, Convolutional Neural Network (CNN) and Graphical Convolutional Network (GCN) models have been developed on odorant molecules-odors and odorant molecules-olfactory receptors using a large set of 5955 molecules, 160 odors and 106 olfactory receptors. The performance of such models is promising with a Precision/Recall Area Under Curve of 0.66 for th

SUBMITTER: Achebouche R 

PROVIDER: S-EPMC9637086 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature