Computational Chemistry Commons^™

Computational Design Of Peptides And Proteins Through Machine Learning Approaches, Emily J. Hendrix

Chemistry and Chemical Biology ETDs

Advancements in machine learning have emerged as a pivotal tool in computational biochemistry, offering new advancements to address challenges in protein structure and function. However, current machine-learning approaches offer limited insight in understanding protein dynamics. The purpose of this work is to combine traditional physics-based computational tools, such as molecular dynamics and coarse-grained simulations, with recently developed AI-driven computational tools to bridge gaps and advance the understanding of proteins in both structural and dynamic aspects. I investigated several approaches such as (i) traditional physics-based methods to study protein conformation and ensembles; (ii) identifying a peptide inhibitor for the PICK1 PDZ …

A Systematic Approach To The Characterization Of Liquid-Vapor Coexistence In Platinum, Meghan K. Lentz

Physics & Astronomy ETDs

Platinum is a material standard used in high pressure and shock compression experiments at Sandia National Laboratories. During experiments, materials are subjected to a very large range of thermodynamic conditions, during which materials regularly enter the liquid-vapor coexistence region. Despite its status as a standard, the region around the liquid-vapor critical point is poorly understood for platinum, with reported critical temperatures spanning approximately 7000 K. In this dissertation we conduct density functional theory based molecular dynamics (DFTMD) simulations for platinum for a range of temperatures and densities near liquid-vapor coexistence. The phase diagram for platinum is refined near the critical …

A Computational Method For Detecting Compound Promiscuity In Early-Stage Pharmaceutical Discovery, John Allen Ringer

Computer Science ETDs

Modern drug discovery and chemical biology research relies heavily on analyzing bioassay data. One of the many challenges in bioassay data analysis is identifying false trails, i.e., chemical compounds which initially appear to have desirable activity but are found to be problematic upon further investigation. Badapple (the BioAssay-Data Associative Promiscuity Pattern Learning Engine) was created over ten years ago to help researchers identify promiscuous compounds and thus avoid a common source of these false trails. Through an effort involving software engineering, cheminformatics, and biomedical data science we have developed Badapple 2.0, which incorporates updated assay records and expanded data semantics. …

Drug Targets Illumination: An Evidence-Based Data Analytics & Informatics Approach To Hypothesis Generation Through Clinical Trials And Genomic Variants, Jeremiah I. Abok

Chemistry and Chemical Biology ETDs

DRUG TARGETS ILLUMINATION: AN EVIDENCE-BASED DATA ANALYTICS & INFORMATICS APPROACH TO HYPOTHESIS GENERATION THROUGH CLINICAL TRIALS AND GENOMIC VARIANTS

Target Illumination Clinical Trial Analytics with Cheminformatics (TICTAC) introduces a scalable data integration pipeline that aggregates multivariate clinical trial evidence to systematically rank disease–target associations. By harmonizing metadata from AACT and employing confidence-scoring techniques, TICTAC facilitates hypothesis-driven target selection for drug discovery, enabling a data-driven approach to therapeutic development. In parallel, a bioinformatics-driven analysis of gnomAD exome mutations in the ZPR1 gene highlights the broader role of genetic variants in disease association. By employing variant effect predictors, protein stability modeling, and …

Secondary Electron Yield Of Metals, Alloys, And Metal Oxides From First Principles Based Monte Carlo Simulations, Raul E. Gutierrez

Electrical and Computer Engineering ETDs

Density Functional Theory (DFT) based Monte Carlo (MC) simulations of the Sec-
ondary Electron Yield (SEY) of metals, alloys, and metal oxides are performed to
find material properties that could help reduce or influence the multipactor effect.
In order to accurately model the SEY of materials, knowledge of the frequency- and
momentum-dependent Energy Loss Function (qDepELF) is required. The qDepELF
is difficult to determine from experiment; however, it can be calculated from first
principles. The DFT-MC approach for simulating the secondary electron genera-
tion, propagation, and emission processes is described herein. Material properties,
which are calculated using DFT and used …

Theoretical And Spectroscopic Insights Into Molybdenum- And Tungsten-Sulfur Bonding, Jesse Lepluart

Chemistry and Chemical Biology ETDs

Molybdenum and tungsten chemistry in biology is dominated by bonding interactions with sulfur and the other chalcogens. The highly-conserved structures of these enzymes’ active sites suggest an early evolution in the history of life, with molybdenum and/or tungsten enzymes likely expressed by the last universal common ancestor. Despite their active site similarities, molybdenum and tungsten enzymes catalyze a diverse array of reactions via equally diverse mechanisms. A deep understanding of the electronic structures of these enzyme active sites and their catalytic intermediates is key to understanding this remarkable chemistry.

The work presented here aims to deepen the understanding of molybdenum …

Learning, Optimizing, And Simulating Fermions With Quantum Computers, Andrew Zhao

Physics & Astronomy ETDs

Fermions are fundamental particles which obey seemingly bizarre quantum-mechanical principles, yet constitute all the ordinary matter that we inhabit. As such, their study is heavily motivated from both fundamental and practical incentives. In this dissertation, we will explore how the tools of quantum information and computation can assist us on both of these fronts. We primarily do so through the task of partial state learning: tomographic protocols for acquiring a reduced, but sufficient, classical description of a quantum system. Developing fast methods for partial tomography addresses a critical bottleneck in quantum simulation algorithms, which is a particularly pressing issue for …

Investigating The Structural Ensemble And Dynamic Interactions Of Protein Interacting With C-Kinase 1 (Pick1) Using Computational Tools, Amy E. Stevens

Chemistry and Chemical Biology ETDs

Protein Interacting with C Kinase-1 (PICK1) is a scaffolding protein that offers a promising solution to support individuals suffering from substance use disorders. While many efforts have been put towards identifying small-molecule drugs with the potential to target PICK1 and ultimately support individuals suffering from the effects of substance use disorders, all efforts have so far fallen short. The purpose of this work is to use computational tools to assist the design of a small-molecule inhibitor of PICK1 while also advancing the knowledge of its biological functions. This work provides information that is currently missing the field by (1) revealing …