Methods for molecular property modeling using virtual data

• US 20050278124A1
• Filed: 03/08/2005
• Published: 12/15/2005
• Est. Priority Date: 06/14/2004
• Status: Abandoned Application

• Current Assignee: Numerate, Inc.
• Original Assignee: Numerate, Inc.
• Inventors: Mydlowec, William, Duffy, Nigel P., Lanza, Guido, Yu, Jessen
• Application Number:
  US11/074,587
• Publication Number:
  US 20050278124A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G01N 33/6803 General methods of protein analysis not limited to specific proteins or families of proteins
• CPC Class Codes: G16C 20/30 Prediction of properties of chemical compounds, compositions or mixtures
• CPC Class Codes: G16C 20/70 Machine learning, data mining or chemometrics

Abstract

Embodiments of the invention provide methods, systems, and articles of manufacture for modeling molecular properties based on information obtained from sources other than direct empirical measurements of the properties. Embodiments of the invention use “virtual data” related to molecular properties to train a molecular properties model. Virtual data about a molecule may include real-valued data (e.g. measurement values falling along a continuous range) or a positive or negative assertion about whether a molecule exhibits a property of interest. Virtual data may be generated using a variety of techniques and may be further characterized by confidence in the accuracy of the virtual data. In addition to virtual data, embodiments of the invention may use “virtual molecules” paired with “virtual data” to train a molecular properties model. The virtual molecules may themselves be generated in a variety of ways.

Claims

1. A method for generating a set of training data used to train a molecular properties model, comprising:

selecting virtual molecules, wherein the virtual molecules are generated using a software application configured to generate representations of physically possible molecules;

assigning the virtual molecules a value for a property of interest being modeled, wherein the property of interest comprises an empirically measurable property, and wherein at least one virtual molecule is assigned an assumed value for the property of interest; and

forming the set of training data from the selected virtual molecules and assigned values for the property of interest.

2. The method of claim 1, wherein the value assigned to a given molecule included in the set of training data comprises an indication that the given molecule is “

active”

or “

inactive”

for the property of interest, a prediction of the activity of the given molecule selected from within a continuous range of values, a prediction that the given molecule is more or less active than another molecule, or a prediction regarding the relative magnitude or differences in the property of interest for two or more molecules included in the set of training data.

3. The method of claim 1, wherein the empirically measurable property comprises a physiological activity, pharmacokinetic property, pharmacodynamic property, physiological or pharmacological activity, toxicity or selectivity;

a chemical property including reactivity, binding affinity, or a property of a specific atom or bond in a molecule;

or a physical property including melting point, solubility, a membrane permeability, or a force-field parameter.

4. The method of claim 1, wherein at least one virtual molecule is generated by selecting a product of a simulation of a chemical reaction pathway or of a plausible chemical reaction simulated by the software application.

5. The method of claim 1, wherein assigning the value to at least one molecule included in the set of training data comprises, running a computer simulation configured to simulate plausible chemical or physical processes involving the at least one molecule or to simulate properties of the at least one molecule.

6. The method of claim 1, wherein assigning the value to at least one molecule included in the set of training data comprises, assigning the most statistically likely value of the property for interest for a randomly selected molecule.

7. The method of claim 1, further comprising:

generating a representation of the molecules included in the set of training data in a form appropriate for a second software application, wherein the second software application is configured to perform a machine learning algorithm using the set of training data; and

providing the set of training data to the second software application,

performing the machine learning algorithm, thereby generating the molecular properties model.

8. The method of claim 7, wherein generating a representation of the molecules included in the set of training data further comprises, including a confidence value for a molecule in the set of training data, wherein the confidence value indicates a measure of confidence in the accuracy of the assigned value relative to the true value for the property of interest and the molecule.

9. The method of claim 7, further comprising:

selecting a test molecule;

generating a representation of the test molecule appropriate for the molecular properties model; and

providing the representation of the test molecule to the molecular properties model; and

generating a prediction about the property of interest for the test molecule.

10. The method of claim 9, further comprising, determining the accuracy of the prediction for the test molecule by carrying out laboratory experimentation using physically existing samples of the test molecule.

11. The method of claim 9, further comprising, determining the accuracy of the prediction for the test molecule by performing a research study using physical samples of the test molecule.

12. A method of generating training data used to train a molecular properties model, the method comprising:

selecting virtual molecules, wherein the virtual molecules are generated using a software application configured to generate representations of physically possible molecules;

assigning the virtual molecules a value for a property of interest being modeled, wherein the property of interest comprises an empirically measurable property, and wherein at least one virtual molecule is assigned an assumed value for the property of interest; and

forming the set of training data from the selected virtual molecules and assigned values for the property of interest;

generating a representation of the molecules included in the set of training data in a form appropriate for a second software application, wherein the second software application is configured to perform a machine learning algorithm using the set of training data; and

providing the set of training data to the second software application, performing the machine learning algorithm, thereby generating the molecular properties model;

selecting a test molecule;

generating a representation of the test molecule appropriate for the molecular properties model; and

providing the representation of the test molecule to the molecular properties model; and

generating a prediction about the property of interest for the test molecule.

13. A method for generating a set of training data used to train a molecular properties model, comprising:

selecting molecules;

assigning the molecules a value for the property of interest being modeled, wherein the property of interest comprises an empirically measurable property, and wherein at least one molecule is assigned an assumed value for the property of interest;

forming the set of training data from the selected molecules and assigned values for the property of interest.

14. The method of claim 13, wherein the value assigned to a given molecule included in the set of training data comprises an indication that the given molecule is “

active”

or “

inactive”

for the property of interest, a prediction of the activity of the given molecule selected from within a continuous range of values, a prediction that the given molecule is more or less active than another molecule, or a prediction regarding the relative magnitude or differences in the property of interest for two or more molecules included in the set of training data.

15. The method of claim 13, wherein the empirically measurable property for the at least one molecule comprises a physiological activity, pharmacokinetic property, pharmacodynamic property, physiological or pharmacological activity, toxicity or selectivity.

16. The method of claim 13, wherein the empirically measurable property for the at least one molecule comprises a chemical property selected from at least one of reactivity, binding affinity, a property of a specific atom or a bond in a molecule.

17. The method of claim 13, wherein the empirically measurable property for the at least one molecule comprises a physical property selected from at least one of a solubility, a membrane permeability, or a force-field parameter.

18. The method of claim 13, wherein at least one virtual molecule is generated by selecting a product of a simulation of a chemical reaction pathway or of a plausible chemical reaction simulated by the software application.

19. The method of claim 13, wherein assigning the value to at least one molecule included in the set of training data comprises, assigning, to the at least one molecule, the most statistically likely value of the property for interest for a randomly selected molecule.

20. The method of claim 13, wherein assigning the value to at least one molecule included in the set of training data comprises, running a computer simulation configured to simulate plausible chemical or physical processes involving the at least one molecule or to simulate properties of the at least one molecule.

21. The method of claim 13, further comprising:

generating a representation of the molecules included in the set of training data in a form appropriate for a second software application, wherein the second software application is configured to perform a machine learning algorithm using the set of training data; and

providing the set of training data to the second software application, performing the machine learning algorithm, thereby generating the molecular properties model.

22. The method of claim 21, wherein generating a representation of the molecules included in the set of training data comprises, determining plausible three-dimensional conformations of the molecules based on the atoms and bonds between atoms present in a given molecule;

or comprises, generating a vector representation of the molecules, wherein the vector representation is configured to encode the structure of a given molecule included in the set of training data.

23. The method of claim 21, wherein generating a representation of the molecules included in the set of training data further comprises:

including a confidence value for a molecule in the set of training data, wherein the confidence value indicates a measure of confidence in the accuracy of the assigned value relative to the true value for the property of interest and the molecule.

24. The method of claim 21, wherein the learning algorithm is selected from one of Boosting, a variant of Boosting, Alternating Decision Trees, the Perceptron algorithm, Winnow, the Hedge Algorithm, an algorithm constructing a linear combination of features or data points, logistic regression, Bayes nets, log linear models, Perceptron-like algorithms, Gaussian processes, probabilistic modeling techniques, regression trees, ranking algorithms, margin based algorithms, or linear, quadratic, convex, conic or semi-definite programming techniques and any combinations thereof.

25. The method of claim 21, further comprising:

selecting a test molecule;

generating a representation of the test molecule appropriate for the molecular properties model; and

providing the representation of the test molecule to the molecular properties model; and

generating a prediction about the property of interest for the test molecule.

26. The method of claim 25, further comprising, determining the accuracy of the prediction for the test molecule by carrying out laboratory experimentation using physically existing samples of the test molecule.

27. The method of claim 25, further comprising, determining the accuracy of the prediction for the test molecule by performing a research study using physical samples of the test molecule.

28. A computer-readable medium containing an executable component that, when executed by a processor, performs operations comprising:

selecting virtual molecules, wherein the virtual molecules are generated using a software application configured to generate representations of physically possible molecules;

assigning the molecules a value for the property of interest being modeled, wherein the property of interest comprises an empirically measurable property, and, wherein at least one virtual molecule is assigned an assumed value for the property of interest; and

forming the set of training data from the selected virtual molecules and assigned values for the property of interest.

29. The computer-readable medium of claim 28, wherein the software application is configured to generate virtual molecules by selecting a product of a simulation of a chemical reaction pathway or of a plausible chemical reaction simulated by the software application.

30. The computer-readable medium of claim 28, wherein assigning the value to at least one molecule included in the set of training data comprises, running a computer simulation configured to simulate plausible chemical or physical processes involving the at least one molecule or to simulate properties of the at least one molecule.

31. The computer-readable medium of claim 28, wherein the operations further comprise:

generating a representation of the molecules included in the set of training data in a form appropriate for a second software application, wherein the second software application is configured to perform a machine learning algorithm using the set of training data; and

providing the set of training data to the second software application,

performing the machine learning algorithm, thereby generating the molecular properties model.

32. The computer-readable medium of claim 31, wherein generating a representation of the molecules included in the set of training data further comprises, including a confidence value for a molecule in the set of training data, wherein the confidence value indicates a measure of confidence in the accuracy of the assigned value relative to the true value for the property of interest and the molecule.

33. The computer-readable medium of claim 31, wherein the operations further comprise:

selecting a test molecule;

generating a representation of the test molecule appropriate for the molecular properties model; and

providing the representation of the test molecule to the molecular properties model; and

generating a prediction about the property of interest for the test molecule.

34. The computer-readable medium of claim 33, wherein the prediction generated for the test molecule is selected from at least one of,

(i) a prediction that the test molecule is “

active”

or “

inactive”

for the property of interest,

(ii) a prediction of the activity of the test molecule within a continuous range of values,

(iii) a prediction that the test molecule is more or less active than another test molecule, or

(iv) a prediction regarding the relative magnitude of the property of interest between two or more molecules.

35. A computer-readable medium containing an executable component that, when executed by a processor, performs operations comprising:

selecting molecules;

assigning the molecules a value for the property of interest being modeled, wherein the property of interest comprises an empirically measurable property, and wherein at least one molecule is assigned an assumed value for the property of interest;

forming the set of training data from the selected molecules and assigned values for the property of interest.

36. The computer-readable medium of claim 35, wherein assigning the value to at least one molecule included in the set of training data comprises:

running a computer simulation configured to simulate plausible chemical or physical processes involving the at least one molecule or to simulate properties of the at least one molecule.

37. The computer-readable medium of claim 35, wherein the operations further comprise:

generating a representation of the molecules included in the set of training data in a form appropriate for a second software application, wherein the second software application is configured to perform a machine learning algorithm using the set of training data; and

providing the set of training data to the second software application, performing the machine learning algorithm, thereby generating the molecular properties model.

38. The computer-readable medium of claim 37, wherein the operations further comprise:

selecting a test molecule;

generating a representation of the test molecule appropriate for the molecular properties model; and

providing the representation of the test molecule to the molecular properties model; and

generating a prediction about the property of interest for the test molecule.

39. A method for evaluating a prediction about a molecule, generated by a molecular properties model, comprising:

receiving the prediction for a test molecule generated by the molecular properties model, wherein the molecular properties model is trained using a set of training data, and wherein the training data comprises;

molecules generated using a first software application configured to generate representations of physically possible molecules; and

a value for a property of interest assigned to each molecule, wherein at least one molecule is assigned an assumed value for the property of interest,

determining the accuracy of the prediction for the test molecule by carrying out experimentation using physically existing samples of the test molecule.