Moleclar property modeling using ranking
AssigneeNumerate, Inc.
InventorNigel P. Duffy
Life of this patent
Estimating the term…
Abstract
Methods and articles of manufacture for modeling molecular properties using data regarding the partial orderings of compound properties, or by considering measurements of compound properties in terms of partial orderings are disclosed. One embodiment provides for constructing such partial orderings from data that is not already in an ordered form by processing training data to produce a partial ordering of the compounds with respect to a property of interest. Another embodiment of the invention may process the modified training data to construct a model that predicts the property of interest for arbitrary compounds.
Claims17
1-10. (canceled)
A method for training a molecular properties model, comprising:
- obtaining a pseudo-partial ordering of molecules, wherein the pseudo partial ordering includes at least a representation of a first and second molecule, ordered relative to one another and a property of interest;
- and
- generating a representation of the molecules included in the pseudo partial ordering of molecules that is appropriate for a selected machine learning algorithm, wherein the pseudo partial ordering of molecules is provided to the selected machine learning algorithm, and wherein executing the selected machine learning algorithm, using the pseudo partial ordering, trains a molecular properties model configured to generate a prediction regarding additional molecules supplied to the model.
24-34. (canceled)
A computer-storage medium containing a program which, when executed by a processor, performs a method for training a molecular properties model, comprising:
- receiving a pseudo-partial ordering of molecules, wherein the pseudo partial ordering includes at least a representation of a first and second molecule, ordered relative to one another and a property of interest;
- and
- generating a representation of the molecules included in the pseudo partial ordering of molecules that is appropriate for a selected machine learning algorithm, wherein the pseudo partial ordering of molecules is provided to the selected machine learning algorithm, and wherein executing the selected machine learning algorithm, using the pseudo partial ordering, trains a molecular properties model configured to generate a prediction regarding additional molecules supplied to the model.
36-40. (canceled)
Dependencies are inferred from the claim text.