Importance

Loading Importance Indicators...

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.

First Claim