US 2022/0277804 A1 · SYSTEM AND METHODS FOR ELECTROSTATIC ANALYSIS WITH MACHINE LEARNING MODEL

Abstract

Systems and methods are described relating to a Poisson Boltzmann machine learning model, which may be executed to predict electrostatic solvation free energy for molecular compounds, such as proteins. Feature data input to the Poisson Boltzmann machine learning model may include multi-weighted colored subgraph centralities, which may be calculated based on edge definitions of pairwise atomic interactions between atoms of a given protein using a generalized exponential function and/or a generalized Lorentz function, either or both of which may be weighted based on atomic rigidity or atomic charge. Predictions of electrostatic solvation free energy performed by the Poisson Boltzmann machine learning model may be used as a basis for ranking candidate compounds for a defined target clinical application.

Claims20

Dependent claims

A system comprising:

a non-transitory computer-readable memory;
and
a processor configured to execute instructions stored on the non-transitory computer-readable memory which, when executed, cause the processor to:
identify a set of compounds based on one or more of a defined target clinical application, a set of desired characteristics, and a defined class of compounds;
pre-process each compound of the set of compounds to generate respective sets of feature data;
process the sets of feature data with a trained Poisson-Boltzmann machine learning model to produce a plurality of predicted electrostatic solvation free energies for each compound of the set of compounds, wherein the sets of feature data include multi-weighted colored subgraph centralities;
identify a subset of the set of compounds based on the plurality of predicted electrostatic solvation free energies;
and
display an ordered list of the subset of the set of compounds via an electronic display.

6 more elements

A method comprising:

calculating, by a processor, a plurality of multi-weighted colored subgraph centralities for a protein;
generating, by the processor, a feature vector that includes the multi-weighted colored subgraph centralities;
and
executing, by the processor, a Poisson-Boltzmann machine learning model to process the feature vector to generate a predicted electrostatic solvation free energy of the protein,

1 more element

A system comprising:

a non-transitory computer-readable memory;
and
a processor configured to execute instructions stored on the non-transitory computer-readable memory which, when executed, cause the processor to:
receive an identifier corresponding to a protein;
generate feature data corresponding to the protein;
and
process the feature data with a trained Poisson-Boltzmann machine learning model to produce a predicted electrostatic solvation free energy of the protein.

4 more elements

Dependencies are inferred from the claim text.

System and methods for electrostatic analysis with machine learning model

Life of this patent

System and methods for electrostatic analysis with machine learning model

Life of this patent

Abstract

Claims20