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 …

Investigation Of Protein-Ligand Binding Interactions Through The Computational Methods Of Molecular Dynamics And Graph Neural Networks, Alexander Benjamin Wroe

Graduate Theses, Dissertations, and Problem Reports (ETD)

Studies in protein ligand binding are a significant focus in the field of medicinal chemistry

and biochemistry, facilitating a greater understanding of the structure-function relationship

of the protein and controlling function via drug development. In this research, we identify

new binding sites for lipids on the microbial membrane protein, proteorhodopsin, and apply

machine learning approaches to accelerate the generation of binding affinity-like data for the

use in the identification of potential small molecule drugs. The platelet activating factor

receptor. Cardiolipin, a lipid in membranes, and proteorhodopsin, a microbial proton pump,

are both commonly found in microbial membranes, but little is …

Design And Application Of Smart Systems To Address Analytical Problems, Lucas B. Ayres

All Dissertations

This dissertation is a multidisciplinary effort that integrates low-cost analytical instrumentation, redox chemistry, and artificial intelligence to overcome existing limitations in the fields of wearable sensing technology, Deep Eutectic Solvents (DES), and antioxidant chemistry. The overall goal behind each implemented strategy is to enhance the accuracy, efficiency, and accessibility of analytical processes and technologies. A general overview of the thesis, along with the research outcomes is included in Chapter One. The theoretical framework of this dissertation is presented in Chapter Two. Chapter Three describes the development of a wearable platform (sensor and instrumentation) to rapidly detect (~20 minutes) S. aureus …

Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang

Dissertations

The development of material discovery and design has lasted centuries in human history. After the concept of modern chemistry and material science was established, the strategy of material discovery relies on the experiments. Such a strategy becomes expensive and time-consuming with the increasing number of materials nowadays. Therefore, a novel strategy that is faster and more comprehensive is urgently needed. In this dissertation, an experiment-guided material discovery strategy is developed and explained using metal-organic frameworks (MOFs) as instances. The advent of 7r-stacked layered MOFs, which offer electrical conductivity on top of permanent porosity and high surface area, opened up new …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Developing And Deploying Data-Driven Tools For Accelerated Design Of Organic Semiconductors, Vinayak Bhat

Theses and Dissertations--Chemistry

Organic semiconductors have gained widespread attention due to their potential applications in flexible, low-cost, lightweight electronics, energy storage and generation technologies, and sensing applications. However, developing new organic semiconductors with improved performance remains a significant challenge due to the vast chemical space of possible molecular and materials structures. Furthermore, the high cost and time-consuming nature of experimental synthesis and characterization hinder the rapid discovery of new materials. To overcome these challenges, this dissertation presents a data-driven approach to organic semiconductor discovery. The primary focus of this work is the development of data-driven tools, namely machine learning models, to predict critical …

State-Based Biological Communication, Nathan Clement

All Theses

Allostery (1) is the process through which proteins self-regulate in response to various stimuli. Allosteric interactions occur between nonadjacent spatially distant residues (1), and they are exhibited through the correlated motions (2) and momenta of participating residues. The location of allosteric sites in proteins can be determined experimentally but computational methods to predict the location of allosteric sites are being developed as well (2-4, 10). Experimental and computational methodologies for locating allosteric sites can be used to design specific targeted drug delivery (5-6, 19), but these methods have not yet …

Development Of Accurate And Efficient Computational Methodologies For Predicting Protein-Ligand And Protein-Protein Binding Free Energies, Alexander Hamilton Williams

Theses and Dissertations--Pharmacy

Computational modeling is an invaluable tool in the drug discovery process either for small ligand or protein therapeutics. The widespread availability of protein X-Ray Crystal and Cryo-Electron Microscopy (Cryo-EM) structures has allowed for more accurate molecular dynamics (MD) simulations that are not reliant on methods such as homology modeling, which may produce structures that require significant computational time to demonstrate their stability. In this thesis we describe several novel methodologies for the computationally efficient modeling of protein/ligand and protein/protein complexes that may be employed within both large-scale virtual screenings and lead compound optimization. These methodologies may also be utilized in …

From Evaluating The Performance Of Approximations In Density Functional Theory To A Machine Learning Design, Pedram Tavazohi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Density-functional theory (DFT) has gained popularity because of its ability to predict the properties of a large group of materials a priori. Even though DFT is exact, there are inaccuracies introduced into the theory due to the approximations in the exchange-correlation (XC) functionals. Over the 50 years of its existence, scientists have tried to improve the design of the XC functionals. The errors introduced by these functionals are not consistent across all types of solid-state materials. In this project, a high throughput framework was utilized to compare the theoretical DFT predictions with the experimental results available in the Inorganic Crystal …