The present invention relates to a computer-implemented method for predicting molecular odors, comprising: providing a first
database comprising three-dimensional (3D) structures and / or chemical structures of one or more olfactory receptors (OR); training a
machine learning model or AI based on the data contained in the first
database; providing a second
database comprising (i) a (3D) structure of one or more odorant molecules and (ii) a corresponding textual description of an odorant
sensation induced by the one or more odorant molecules in the human subject; training a
machine learning (ML) model or
artificial intelligence (AI) based on the data contained in the second database; fitting the (3D) structure of the one or more odorant molecules with the (3D) structure of the one or more ORs, thereby determining an affinity
score for each fit combination of the one or more odorant molecules with the respective olfactory receptors, where the fitting includes using and training an ML model or AI, and where the affinity
score is indicative of a degree of fit of the respective odorant molecules with the ORs; creating a comprehensive third database or third data embedding structure / layer comprising data of the first database, data of the second database, and affinity scores determined for each fit combination of a respective odorant molecule with a respective
olfactory receptor; providing a (3D) structure of at least the first target odorant molecules not contained in the second database; and predicting, by
machine learning or AI, an
odor sensation induced in the human subject by at least the first target
odor molecule in view of the data contained in the third database or the third data embedding structure / layer.