Subject Area
Biophysics, Computer Science, Molecular Biology
Abstract
We investigate machine learning and electrostatic methods to predict biophysical properties of proteins, such as solvation energy and protein ligand binding affinity, for the purpose of drug discovery/development. We focus on the Poisson-Boltzmann model and various high performance computing considerations such as parallelization schemes.
Degree Date
Spring 2024
Document Type
Dissertation
Degree Name
Ph.D.
Department
Mathematics
Advisor
Weihua Geng
Second Advisor
Wei Cai
Third Advisor
Andrea Barreiro
Fourth Advisor
John Nemunaitis
Number of Pages
125
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Sliheet, Elyssa, "Predicting Biomolecular Properties and Interactions Using Numerical, Statistical and Machine Learning Methods" (2024). Mathematics Theses and Dissertations. 25.
https://scholar.smu.edu/hum_sci_mathematics_etds/25
Included in
Biological and Chemical Physics Commons, Medicine and Health Sciences Commons, Partial Differential Equations Commons, Theory and Algorithms Commons
