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EP-4740212-A1 - A COMPUTER MODEL OF THE SENSE OF SMELL

EP4740212A1EP 4740212 A1EP4740212 A1EP 4740212A1EP-4740212-A1

Abstract

The invention relates to computer implemented methods for the prediction of the smell of a molecule, comprising providing a first database comprising the structure, preferably the three- dimensional (3D) and/or chemical structure, of one or more olfactory receptor (OR), training a machine learning model or AI on the data comprised within the first database, providing a second database comprising (i) the (3D) structure of one or more odorant molecule and (ii) a respective textual description of the odor sensation induced by said one or more odor molecule in a human subject, training a machine learning (ML) model or artificial intelligence (AI) on the data comprised within the second database, fitting the (3D) structure of the one or more odor molecule to the (3D) structure of the one or more OR, thereby determining an affinity score for each fitted combination of the one or more odor molecule with a respective olfactory receptor, wherein the fitting comprises the use and training of a ML model or AI, wherein the affinity score is indicative for the degree of fit of a respective odor molecule to the OR, creating a comprehensive third database or third data embedding structure/layer comprising the data of the first database, the second database and the affinity score determined for each fitted combination of a respective odor molecule with a respective olfactory receptor, providing the (3D) structure of at least a first odor molecule of interest, which is not comprised within the second database, and predicting an odor sensation induced by the at least first odor molecule of interest in a human subject using machine learning or AI considering the data comprised within the third database or third data embedding structure/layer.

Inventors

  • SHALEV, Liron

Assignees

  • Shalev, Liron

Dates

Publication Date
20260513
Application Date
20240702

Claims (14)

  1. 1 . A computer implemented method for the prediction of the smell of a molecule, comprising the steps of: a. Providing a first database comprising information regarding the structure, preferably the three-dimensional (3D) and/or chemical structure, of one or more olfactory receptor (OR), b. Training a machine learning model or Al (receptor encoder) on the data comprised within the first database, c. Providing a second database comprising (i) the (3D) structure of one or more odor molecule and (ii) a respective textual description of the odor sensation induced by said one or more odor molecule in a human subject, d. Training a machine learning (ML) model or artificial intelligence (Al) (odorant encoder) on the data comprised within the second database, e. Fitting the (3D) structure of the one or more odor molecule to the (3D) structure of the one or more olfactory receptor (OR), preferably to the ligand binding site of the one or more OR, thereby determining an affinity score for each fitted combination of the one or more odor molecule with a respective olfactory receptor, wherein the fitting comprises the use and training of a ML model or Al (pairwise interactions encoder), wherein the (numerical value of the) affinity score is indicative for the degree of fit of a respective odor molecule to the olfactory receptor (OR), preferably to the ligand binding site of a respective OR, and is preferably indicative for the capability of said respective odor molecule to (i) bind or interact with the respective olfactory receptor and/or (ii) to activate the respective olfactory receptor, f. Creating a comprehensive third database or third data embedding structure comprising the data of the first database, the second database and the affinity score determined for each fitted combination of a respective odor molecule with a respective olfactory receptor, g. Providing the (3D) structure of at least a first odor molecule of interest, which is not comprised within the second database, and h. Predicting an odor sensation induced by the at least first odor molecule of interest in a human subject using machine learning or Al considering the data comprised within the third database or third data embedding structure.
  2. 2. The computer implemented method according to claim 1 , wherein h. predicting an odor sensation induced by the at least first odor molecule of interest in a human subject is achieved by using one or more of the machine learning model(s) or Al trained in steps b., d. and e. of claim 1 , to predict, based on the (3D) structure of the at least first odor molecule of interest: - at least one odor molecule comprised within the third database or third data embedding structure that has the highest similarity of affinity score(s) for one or more olfactory receptor(s) comprised within the third database or third data embedding structure to the at least first odor molecule of interest, and/or - an odor sensation induced by the at least first odor molecule of interest in a human subject, wherein the odor sensation preferably corresponds to one or more textual descriptions of an odor sensation induced by an odor molecule in a human subject comprised within the third database or third data embedding structure.
  3. 3. The computer implemented method according to claim 1 or 2, wherein step h. predicting an odor sensation induced by the at least first odor molecule of interest in a human subject comprises: (I) Comparing the (3D) structure of the at least first odor molecule of interest and one or more of the odorant molecules comprised within the second or third database using the ML model or Al (odorant encoder) trained in d., (II) Fitting the (3D) structure of the at least first odor molecule of interest to the (3D) structure of one or more of the olfactory receptor (OR), preferably of the ligand binding site of said OR, using the ML model or Al (pairwise interactions encoder) trained in e., thereby determining an affinity score for each fitted combination of the at least first odor molecule of interest with the respective OR, (III) Comparing one or more of the affinity score(s) determined in step (II) for each fitted combination of the at least first odor molecule of interest with the one or more olfactory receptor (OR), with the affinity score(s) for each combination of the one or more OR with one or more odor molecule comprised within the third database or third data embedding structure, (IV) thereby identifying at least one odor molecule comprised within the third database or third data embedding structure possessing the highest similarity of affinity score(s) for the one or more OR to the at least first odor molecule of interest, wherein the respective textual description comprised within the third database or third data embedding structure/layer of the odor sensation induced in a human subject by said one or more odor molecule(s) identified in (IV) is indicative of the odor sensation induced by the at least first odor molecule of interest in a human subject.
  4. 4. The method according to any one of the preceding claims, wherein finally the at least first odor molecule of interest is chemically synthesized, based on the (3D) structure provided in step g. of claim 1.
  5. 5. The method according to any one of the preceding claims, wherein the at least one olfactory receptor is selected from the group comprising olfactory receptors (ORs), formyl peptide receptors (FPRs), the guanylyl cyclase GC-D, the vomeronasal receptors (V1 Rs and V2Rs), and trace amine-associated receptors (TAARs).
  6. 6. The method according to any one of claims, wherein the (3D) structure of a olfactory receptor and/or odor molecule was determined using experimental crystallography information, computer simulation, an artificial intelligence (Al) and/or a machine learning (ML) model trained on experimental data, crystallography information, genetic sequence information of the respective olfactory receptor and/or odor molecule, or any combination of thereof.
  7. 7. The method according to any one of the preceding claims, wherein in step e. an affinity score is determined only for a subset of the odor molecules and/or olfactory receptors comprised within the first, second and/or third database by identifying dependencies and/or correlations between the different olfactory receptors and/or odor molecules using statistical and/or machine learning-based approaches, optionally by setting a threshold for the correlation coefficient between different receptors and/or odor molecules by assigning an affinity score for the 25-100 least correlated receptors and/or odor molecules.
  8. 8. The method according to any one of the preceding claims, wherein in step d. an affinity score is determined using computer physics simulation, an ML algorithm, and/or an algorithm that combines statistical and ML methods with a (computational) simulation element.
  9. 9. The method according to any one of the preceding claims, wherein the at least first odor molecule of interest comprises a mixture of at least a first and a second odor molecule of interest, and wherein in step h., in addition to (i) the (3D) structures of the least first and second odor molecule of interest, (ii) the relative percentage of each at least first and second odor molecule in the mixture’s vapor and/or (iii) the statistical probability of each odor molecule to interact with the olfactory receptors in the nose of a human subject is provided, and wherein step h. comprises predicting an odor sensation induced in a human subject by the mixture of the at least first and second odor molecules of interest.
  10. 10. The method according to the preceding claim, wherein the relative percentage of the at least first and second odor molecule in the mixture’s vapor is determined by further considering the relative percentage of the least first and second odor molecule in the mixture and/or the respective vapor pressure of the mixture.
  11. 11 . A computer implemented method for predicting the (3D) structure of an odor molecule of interest capable of inducing an odor sensation of interest in a human subject and based on its binding affinity to at least one olfactory receptor, comprising the steps a.-f. of the method according to claim 1 , and further comprising the steps of G. Selecting from the third database or third data embedding structure a textual description of an odor sensation of interest induced by a first odor molecule, H. Determining from the third database or third data embedding structure one or more olfactory receptor(s) with the highest affinity score(s) for the first odor molecule, I. Predicting from the data of the third database or third data embedding structure the (3D) structure of an odor molecule of interest, which is not comprised within the third database, wherein the predicted (3D) structure is modelled/predicted to fit with a high affinity score to the (3D) structure of the ligand binding site of the one or more olfactory receptor(s) selected in H., wherein the high affinity score of the predicted (3D) structure of the odor molecule of interest is indicative for the capability of said odor molecule of interest to bind or interact with the ligand binding site of the respective one or more olfactory receptor(s), and/or to activated the one or more olfactory receptor(s), thereby inducing an odor sensation in a human subject similar to the first odor molecule.
  12. 12. The method according to claim 11 , wherein the prediction method used in step H. is employed by an Al and/or a machine learning (ML) model, preferably a gradient-less ML model, preferably comprising a genetic algorithm to generate molecules and/or to optimize the prediction toward a given numeric vector, or a gradient-based ML model, preferably comprising a generative adversarial network (GAN) or a diffusion probabilistic model.
  13. 13. The method according to claim 12, wherein the Al and/or a ML model is or comprises one or more of the machine learning model(s) or Al trained in b., d. and e. of claim 1 .
  14. 14. The method according to claims 11-13, wherein the predicted (3D) structure of the odor molecule of interest is used to chemically synthesize the predicted molecule structure.

Description

A COMPUTER MODEL OF THE SENSE OF SMELL DESCRIPTION The invention relates to computer implemented methods for the prediction of the smell of a molecule, comprising providing a first database comprising the three-dimensional (3D) and/or chemical structure of one or more olfactory receptor (OR), training a machine learning model or Al on the data comprised within the first database, providing a second database comprising (i) the (3D) structure of one or more odorant molecule and (ii) a respective textual description of the odor sensation induced by said one or more odor molecule in a human subject, training a machine learning (ML) model or artificial intelligence (Al) on the data comprised within the second database, fitting the (3D) structure of the one or more odor molecule to the (3D) structure of the one or more OR, thereby determining an affinity score for each fitted combination of the one or more odor molecule with a respective olfactory receptor, wherein the fitting comprises the use and training of a ML model or Al, wherein the affinity score is indicative for the degree of fit of a respective odor molecule to the OR, creating a comprehensive third database or third data embedding structure/layer comprising the data of the first database, the second database and the affinity score determined for each fitted combination of a respective odor molecule with a respective olfactory receptor, providing the (3D) structure of at least a first odor molecule of interest, which is not comprised within the second database, and predicting an odor sensation induced by the at least first odor molecule of interest in a human subject using machine learning or Al considering the data comprised within the third database or third data embedding structure/layer. BACKGROUND OF THE INVENTION The perception of odors is highly complex an involves after the entrance of odorants through the nose the recognition through olfactory sensory neurons (OSNs), which comprise numerous different kinds of olfactory receptors (ORs). The neural signal of the OSNs is subsequently transmitted and processed by the olfactory cortex of the brain (Fleischer et al., 2009). Generally, the group of olfactory receptors (ORs) comprises the receptor families of odorant receptors (ORs), the vomeronasal receptors (V1 Rs and V2Rs), trace amine-associated receptors (TAARs), formyl peptide receptors (FPRs), and the guanylyl cyclase GC-D, which are majorly G protein-coupled receptor proteins (GPCRs) (Fleischer et al., 2009). At present it is noted in the art that the three dimensional structure of odorants, and optionally also their physicochemical properties, such as the boiling and vapor point and the molecular polarity, have an influence on the olfactory experience (smell) that is perceived by a subject. This correlation between the chemical structures and properties of odorants and the induced biological response is termed the Quantitative Structure Activity Relationship (QSAR) model (Dearden, 1994; Hau, and Connell, 1998). However, in the prior art there is still a lack of precise methods and automated approaches to analyze and reliably determine the correlation between the structure of a substances and the olfactory perception it induces in a subject. In light of the prior art there remains a significant need in the art to provide improved means for the prediction of an odor or olfactory perception of molecules. SUMMARY OF THE INVENTION In light of the prior art the technical problem underlying the present invention is to provide alternative and/or improved means for the prediction of an odor or olfactory perception of molecules. This problem is solved by the features of the independent claims. Preferred embodiments of the present invention are provided by the dependent claims. The present invention relates in a first aspect to a computer implemented method for the prediction of the smell (odor/olfactory sensation) of a molecule, comprising the steps of: a. Providing a first database comprising information regarding the structure, preferably the three-dimensional (3D) and/or chemical structure, of one or more olfactory receptor (OR), preferably wherein the database comprises information on the 3D structure of the ligand (or agonist) binding site of the one or more olfactory receptor, b. Training a machine learning model or Al (receptor encoder) on the data comprised within the first database, c. Providing a second database comprising (i) the (3D) structure of one or more odor (odorant) molecule and preferably (ii) a respective textual description of the odor sensation induced by said one or more odor molecule in a human subject, d. Training a machine learning (ML) model or artificial intelligence (Al) (odorant encoder) on the data comprised within the second database, d.1 . Optionally providing data on the interaction of one or more olfactory receptor molecules from the first database with one or more odor molecule from the second database, e. Fitting