Computational Chemistry Commons^™

Unlocking The Secret To Weight Loss: Discovering The Most Effective Green Tea Products, Seoyeon Kim

SACAD: John Heinrichs Scholarly and Creative Activity Days

To find the most effective green tea product that can replace

weight loss drugs, we compared the amount of catechin in six

different green tea products. The result was green tea latte

powder contained a significantly small amount of catechin

compared to other products, and the dried pearl green tea leaves

had the highest amount of catechin. Also, the unexpected result

was that green tea supplements had less amount of catechin

compared to dried leaves or even tea bags that are commonly

sold in markets.

Accurate Characterization Of Binding Kinetics And Allosteric Mechanisms For The Hsp90 Chaperone Inhibitors Using Ai-Augmented Integrative Biophysical Studies, Chao Xu, Xianglei Zhang, Lianghao Zhao, Gennady M. Verkhivker, Fang Bai

Mathematics, Physics, and Computer Science Faculty Articles and Research

The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure–kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate …

Multi-Scale Simulations Of Dynamic Protein Structures And Interactions, Yumeng Zhang

Doctoral Dissertations

Intrinsically disordered proteins (IDPs) are functional proteins that lack stable tertiary structures in the unbound state. They frequently remain dynamic even within specific complexes and assemblies. IDPs are major components of cellular regulatory networks and have been associated with cancers, diabetes, neurodegenerative diseases, and other human diseases. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for mechanistic understanding of IDPs in biology, diseases, and therapeutics. However, accurate simulation of the heterogeneous ensembles and dynamic interactions of IDPs is extremely challenging because of both the prohibitive computational cost and demanding force field …

Mechanistic Investigation Of C—C Bond Activation Of Phosphaalkynes With Pt(0) Complexes, Roberto M. Escobar, Abdurrahman C. Ateşin, Christian Müller, William D. Jones, Tülay Ateşin

Research Symposium

Carbon–carbon (C–C) bond activation has gained increased attention as a direct method for the synthesis of pharmaceuticals. Due to the thermodynamic stability and kinetic inaccessibility of the C–C bonds, however, activation of C–C bonds by homogeneous transition-metal catalysts under mild homogeneous conditions is still a challenge. Most of the systems in which the activation occurs either have aromatization or relief of ring strain as the primary driving force. The activation of unstrained C–C bonds of phosphaalkynes does not have this advantage. This study employs Density Functional Theory (DFT) calculations to elucidate Pt(0)-mediated C–CP bond activation mechanisms in phosphaalkynes. Investigating the …

Β-Sheets Mediate The Conformational Change And Allosteric Signal Transmission Between The Aslov2 Termini, Sian Xiao, Mayar Terek Ibrahim, Gennady M. Verkhivker, Brian D. Zoltowski, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

Avena sativa phototropin 1 light-oxygen-voltage 2 domain (AsLOV2) is a model protein of Per-Arnt-Sim (PAS) superfamily, characterized by conformational changes in response to external environmental stimuli. This conformational change begins with the unfolding of the N-terminal A'α helix in the dark state followed by the unfolding of the C-terminal Jα helix. The light state is characterized by the unfolded termini and the subsequent modifications in hydrogen bond patterns. In this photoreceptor, β-sheets are identified as crucial components for mediating allosteric signal transmission between the two termini. Through combined experimental and computational investigations, the Hβ …

Confirmation Of Anomalous-Heat Report, Steven B. Krivit, Melvin H. Miles

Journal of Electrochemistry

This study identifies, for the first time, critical calculation errors made by Nathan Lewis and his co-authors, in their study presented on May 1, 1989, at the American Physical Society meeting in Baltimore, Maryland. Lewis et al. analysed calorimetrically measured heat results in nine experiments reported by Martin Fleischmann and his co-authors. According to the Lewis et al. analysis, each of the experiments, where calculated for no recombination, showed anomalous power losses. When we used the same raw data, our corrected calculations indicate that each experiment showed anomalous power gains. As such, these data suggest the possibility of a new, …

Automated Workflow For Redox Potentials And Acidity Constants Calculations From Machine Learning Molecular Dynamics, Feng Wang, Jun Cheng

Journal of Electrochemistry

Redox potentials and acidity constants are key properties for evaluating the performance of energy materials. To achieve computational design of new generation of energy materials with higher performances, computing redox potentials and acidity constants with computational chemistry have attracted lots of attention. However, many works are done by using implicit solvation models, which is difficult to be applied to complex solvation environments due to hard parameterization. Recently, ab initio molecular dynamics (AIMD) has been applied to investigate real electrolytes with complex solvation. Furthermore, AIMD based free energy calculation methods have been established to calculate these physical chemical properties accurately. However, …

Measurements Of Rate Constant For Electrode Reactions, Lian-Huan Han, Jia-Yao Guo, Miao-Miao Cui

Journal of Electrochemistry

Standard electron-transfer rate constant is one of the intrinsic properties for an electrochemical reaction, which is significant in the study of electrode kinetics. It is a key criterion for one to clarify the mechanism and pathway of a specific electrochemical reaction, and to screening and design the electrocatalysts and battery materials. Herein, we will introduce the measuring methods of rate constant for electrode reactions, including polarization curve, rotating disk electrode, ultramicroelectrode, scanning electrochemical microscopy, electrochemical impedance spectroscopy, current step, potential step and cyclic voltammetry, etc., to provide a guide to investigate electrode kinetics for graduate students and researchers in the …

Joint Time-Frequency Analysis: Taking Charge Penetration Depth And Current Spatial Distribution In The Single Pore As An Example, Nan Wang, Qiu-An Huang, Wei-Heng Li, Yu-Xuan Bai, Jiu-Jun Zhang

Journal of Electrochemistry

In recent years, joint time-frequency analysis has once again become a research hotspot. Supercapacitors have high power density and long service life, however, in order to balance between power density and energy density, two key factors need to be considered: (i) the specific surface area of the porous matrix; (ii) the electrolyte accessibility to the intra-pore space of porous carbon matrix. Electrochemical impedance spectra are extensively used to investigate charge penetration ratio and charge storage mechanism in the porous electrode for capacitance energy storage. Furthermore, similar results could be obtained by different methods such as stable-state analysis in the frequency …

Rational Design Of Peptide-Based Materials Informed By Multiscale Molecular Dynamics Simulations, Dhwanit Rahul Dave

Dissertations, Theses, and Capstone Projects

The challenge of establishing a sustainable and circular economy for materials in medicine and technology necessitates bioinspired design. Nature's intricate machinery, forged through evolution, relies on a finite set of biomolecular building blocks with through-bond and through-space interactions. Repurposing these molecular building blocks requires a seamless integration of computational modeling, design, and experimental validation. The tools and concepts developed in this thesis pioneer new directions in peptide-materials design, grounded in fundamental principles of physical chemistry. We present a synergistic approach that integrates experimental designs and computational methods, specifically molecular dynamics simulations, to gain in-depth molecular insights crucial for advancing the …

Evaluating The Reliability And Accuracy Of Alchemical Binding Free Energy Methods And Calculations, Fnu Sheenam

Dissertations, Theses, and Capstone Projects

Molecular recognition plays a crucial role in various biological processes, such as enzymatic reactions, signal transduction, and genetic information processing. Investigating how proteins selectively bind to their partners is an active research area, but there is a lack of experimental details on protein structures and interactions in molecular complexes. Computational techniques based on macromolecular structures offer a way to predict protein-ligand interactions and explore their recognition mechanisms. Estimating binding affinities, particularly through alchemical binding free energy calculations, has become valuable in supporting drug discovery. This work introduces new methodologies, utilizing the Alchemical Transfer Method, to address issues like poor convergence …

The Top Ten Scientific Questions In Electrochemistry, Chinese Society Of Electrochemistry

Journal of Electrochemistry

No abstract provided.

De Novo Drug Design Using Transformer-Based Machine Translation And Reinforcement Learning Of An Adaptive Monte Carlo Tree Search, Dony Ang, Cyril Rakovski, Hagop S. Atamian

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards …

Analyzing Novel Metal Alloys For Glucose Sensing And Electrocatalysis, Anna Grace Boddy

Theses and Dissertations

In pharmaceutical and medicinal chemistry, metals and metal alloys often receive less attention compared to biological or organic compounds due to many factors including toxicity in the body for drug development or the cost of these metals. However, metals can play an important role in pharmaceuticals, having an impact on original cancer drugs, such as platinum used for head and neck tumors. Electrocatalysis is also another topic that receives less attention over topics such as chromatography in pharmaceuticals due to its potential toxic catalysts and voltages that could be harmful to the body. Electrocatalytic sensors can play an important role …

Development Of Stochastic And Time-Dependent Quantum Chemical Methods For Accurate Description Of Light-Matter Interactions With Applications In Photoionization And Inverse Photoemission In Nanomaterials, Nicole Spanedda

Dissertations - ALL

There are three main focuses of this work. First, the theoretical details of the Stratified Stochastic Enumeration of Molecular Orbitals (SSE-MO) method is presented, along with its application for calculating ionization potentials (IPs) of quantum dots. The SSE-MO method can readily be applied for the purpose of efficiently and accurately calculating ionization potentials, by constructing the frequency-dependent self-energy operator and then subsequently, solving the associated Dyson equation. Constructing the frequency-dependent self-energy operator is challenging because the scaling of the computational cost with respect to system size, becomes prohibitive for large systems, such as quantum dots. This is due to the …

Predicting The Reactions Of Cs2, Ocs, And Co2 With Group Iv And Group Vi Transition Metal Oxides, Marissa Shea Blair, Zachary Ryan Lee Phd, David A. Dixon Phd

Posters-at-the-Capitol

Building on a recent serious of high level electronic structure studies of Lewis acid gas reactions with metal oxide sorbents, DFT (B3LYP and ωB97X-D) and CCSD(T) methods are being used to predict the Lewis acid-base addition (physisorption) and formation of metal oxide carbonate/thiocarbonate formation (chemisorption) reactions of CS2, OCS, and CO2 of CS2, OCS, and CO2 with Group IV (MO2)n and Group VI (MO3)n (n = 1 - 3) nanoclusters. For the Group IV oxides, chemisorption to form terminal carbonates and thiocarbonates is predicted to be the most favored, with thiocarbonate ligand binding energies slightly more exothermic than their carbonate …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Assessing Interatomic Potentials For Molecular Dynamics Simulation Of Soybean Oil Pyrolysis, Tanner Garrett Rust

MSU Graduate Theses

The world today relies on hydrocarbon combustion for many reasons, including its high energy density that provides ease of transportation. However, hydrocarbons sourced from fossil fuels are not expected to last forever. Biodiesel, a renewable alternative, has many attractive benefits but comes with other downsides. Biodiesel can gel in cold environments and may leave residue in an engine. Pyrolysis of biodiesel has shown promise in addressing these common detriments. Inducing pyrolysis on biodiesel feedstock (commonly soybean oil in the USA) would be an attractive option presuming it continues to produce fossil fuel analogs similar to biodiesel pyrolysis. Herein, Langevin molecular …

Intelligent Control Based On Bp Artificial Neural Network For Electrochemical Nitrate Removal, Xin-Wan Zhang, Guang-Yuan Meng, Li-Qiang Fang, Ding-Ming Chang, Tong Li, Jin-Wen Hu, Peng Chen, Yong-Di Liu, Le-Hua Zhang

Journal of Electrochemistry

Achieving effective control of parameters in the process of nitrate wastewater treatment is critical to electrochemical water treatment. The powerful nonlinear mapping ability, self-adaptation and self-learning ability of neural network technology can optimize the electrochemical processing. However, there are few researches in this direction. Hence, based on the test data of the electrochemical reduction of nitrate, an electrochemical prediction model was established by using the BP neural network algorithm. Considering the correlation of various parameters in the electrochemical process, the reaction time, initial nitrate nitrogen concentration, pH and current density were determined as the input layer of the BP neural …

Search For Osme Bonds With Π Systems As Electron Donors, Xin Wang, Qingzhong Li, Steve Scheiner

Chemistry and Biochemistry Faculty Publications

The Osme bond is defined as pairing a Group 8 metal atom as an electron acceptor in a noncovalent interaction with a nucleophile. DFT calculations with the ωB97XD functional consider MO₄ (M = Ru, Os) as the Lewis acid, paired with a series of π electron donors C₂H₂ , C₂H₄ , C₆H₆ , C₄H₅N, C₄H₄O, and C₄H₄S. The calculations establish interaction energies in the range between 9.5 and 26.4 kJ/mol. Os engages in stronger interactions than does Ru, …

Computational Investigations Of Bond Breaking Processes Using Dft And Td-Dft Approaches., Saurav Parmar

Electronic Theses and Dissertations

The efficient application of DFT and TD-DFT has been harnessed to study bond-breaking processes in some molecules which play a prominent role in enzymatic reactions. The first application includes Radical S-adenosyl methionine (SAM) enzymes which are fundamentally important sources of organic radicals to initiate diverse radical reactions. Recently a bio-organometallic intermediate (Ω) that contains an Fe‒C bond has been characterized and shown to be a common feature of radical SAM enzymes. The strength of Fe‒C bond in Ω has been computed using broken-symmetry density functional theory (BS‒DFT). Additionally, Fe‒C bond dissociation energy (BDE) in Ω has been compared to that …

Computational Quantum Chemistry Studies Of The Stabilities Of Radical Adducts Formed During The Oxidation Of Melatonin Derivatives, James Horne

Electronic Theses and Dissertations

Melatonin is a natural antioxidant that has been investigated for properties as a potential spin trap to identify short-lived free radicals. Computational quantum chemistry studies have been performed for the oxidation of melatonin to N¹-acetyl-N²-formyl-5-methoxykynuramine. This research focused on modification of melatonin into derivatives and analyzing the change in total molecular energy from melatonin to its oxidation product, as well as the corresponding derivatives. Each of the molecular geometries were optimized at the DFT/B3LYP/6-31G(d), DFT/B3LYP/cc-pVXZ (X = D, T), HF/6-31G(d), HF/cc-PVXZ (X = D, T), MP2/6-31G(d), and MP2/cc-PVXZ (X = D, T) levels of theory. …

Computation-Assisted Molecular Discovery For Biomedical Applications: Seeking Small Molecules And Dna Sequences With High Affinity Target Binding, Payam Kelich

Open Access Theses & Dissertations

Binding affinity between two molecules is an essential property in drug and sensor discovery. Several computational and experimental methods exist to find molecules with high binding affinities to desired target molecules. These methods are often complementary, where fast computational methods can be used for the initial screening of molecules, and experimental methods can then screen and determine the molecules of interest and sometimes define the structures of bound complexes. After these steps, computational methods, like molecular dynamics (MD) simulations, can provide detailed insights into atomic interactions and binding, and machine learning approaches can analyze experiment-derived data to discern patterns and …

Investigating The Diffusion Of Solid Yttria Into Solid Zirconia For The Formation Of Yttria Stabilized Zirconia, Logan Thomas Ockershausen

Seton Hall University Dissertations and Theses (ETDs)

A molecular dynamics simulation of yttria-stabilized zirconia (YSZ) is reported in order to analyze the diffusion of the oxygen atoms from the yttria into the zirconia from an initial structure containing a layer of yttria on zirconia. The simulation was performed starting from a pure zirconia slab which was amorphized at the surface followed by placing a slab of yttria on top and a vacuum layer above that. The yttria/zirconia system was simulated at increasing temperatures to 1500K under canonical (NVT) constraints for 6.98 ns until the yttrium atom diffusion was found to occur. The final run was analyzed for …

Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes, Nelson I. Evbarunegbe

Masters Theses

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities.

Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this …

Applying Density Functional Theory Simulations To Study The Charge Balancing And Structure Directing Roles Of Fluoride In Zeolite Synthesis, Tongkun Wang

Doctoral Dissertations

Zeolites represent a major cornerstone of today’s energy industry as the most-used petrochemical catalyst by weight in the world. Constituted by tetrahedra of T-atoms including Si, Al, Ge and Ti, zeolites form a huge family of nano-porous crystalline materials which also provide reliable candidates for novel, energy related applications such as efficient separations, hydrogen-purifying/storing and conversions from biomass to biofuel. However, the formation mechanism of zeolite is still not clear, as synthesis processes are complicated by requirements including structure directing agents (SDAs), hydroxide or fluoride medium, and experimental conditions like temperature. Attempts for designing new zeolite structures still fall in …

Atomistic Simulations Of Intrinsically Disordered Protein Folding And Dynamics, Xiping Gong

Doctoral Dissertations

Intrinsically disordered proteins (IDPs) are crucial in biology and human diseases, necessitating a comprehensive understanding of their structure, dynamics, and interactions. Atomistic simulations have emerged as a key tool for unraveling the molecular intricacies and establishing mechanistic insights into how these proteins facilitate diverse biological functions. However, achieving accurate simulations requires both an appropriate protein force field capable of describing the energy landscape of functionally relevant IDP conformations and sufficient conformational sampling to capture the free energy landscape of IDP dynamics. These factors are fundamental in comprehending potential IDP structures, dynamics, and interactions. I first conducted explicit solvent simulations to …

Contributions Of Tunneling In 8Π-6Π Electrocyclic Cascade Reactions Of Bicyclo[4.2.0]Octa-2,4-Diene Moieties, Ishika Jain, Claire Castro, William L. Karney

Featured Student Work

Six-electron electrocyclic reactions usually require relatively high temperatures; however recent research has shown that such reactions can occur at significantly lower temperatures in biosynthetic and biomimetic pathways. Pathways resulting in bicyclo[4.2.0]octa-2,4-diene moieties arise from thermally allowed 8π-6π electrocyclization cascade reactions of 1,3,5,7-octatetraenes, as in the biosynthesis of endiandric acids, elysiapyrones, and numerous other natural products. We report multidimensional tunneling calculations to explore the possible contribution of heavy-atom tunneling (e.g. by carbon) to biosynthetic pathways and biomimetic syntheses, and thus to provide a more complete picture of biochemical kinetics. M06-2X/cc-pVDZ calculations on the 8π-6π cascade cyclizations of methylated octatetraene model systems …

An Insight Into The Physicochemical, Drug-Likeness, Pharmacokinetics And Toxicity Profile Of Kigelia Africana (Lam) Bioactive Compounds, Sulyman Olalekan Ibrahim, Halimat Yusuf Lukman, Marili Funmilayo Zubair, Oluwagbemiga Tayo Amusan, Fatimah Ronke Abdulkadri, Bashir Lawal, Lateefat Bello Abdulfatah, Olubunmi Atolani

Al-Bahir Journal for Engineering and Pure Sciences

Kigelia africana plant is multipurpose plant whose therapeutic potential has been thoroughly investigated. The physicochemical, solubilities, ADMET, pharmacological, and drug-like properties of this plant have not been reported in details. This study makes use of the information that is currently known on the chemical make-up of the plant to forecast its overall toxicity as well as the potential for the phytochemicals it contains to be employed in medication discovery. The study also employed free web servers for the lipophilicity, water solubility, drug-likness, bioavailability score, medicinal chemistry and toxicological profiling of the compounds of K. africana. Artemether, a known antimalaria …

Biomimetic Modifications On The Bridging Cys Residue Ligand Of An Feni Hydrogenase, Madeline Kesner

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.