Role Of Computational Chemistry Research Articles

The Role of Computational Chemistry in Exploring the Structure and Properties of Chloroquine Derivatives

The Role of Computational Chemistry in Exploring the Structure and Properties of Chloroquine Derivatives

Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation: A Tool for Structure-Based Drug Design and Discovery.

Quantum Mechanics (QM) is the physics-based theory that explains the physical properties of nature at the level of atoms and sub-atoms. Molecular mechanics (MM) construct molecular systems through the use of classical mechanics. So, when combined, hybrid quantum mechanics and molecular mechanics (QM/MM) can act as computer-based methods that can be used to calculate the structure and property data of molecular structures. Hybrid QM/MM combines the strengths of QM with accuracy and MM with speed. QM/MM simulation can also be applied for the study of chemical processes in solutions, as well as in the proteins, and has a great scope in structure-based drug design (SBDD) and discovery. Hybrid QM/MM can also be applied to HTS to derive QSAR models. Due to the availability of many protein crystal structures, it has a great role in computational chemistry, especially in structure- and fragment-based drug design. Fused QM/MM simulations have been developed as a widespread method to explore chemical reactions in condensed phases. In QM/MM simulations, the quantum chemistry theory is used to treat the space in which the chemical reactions occur; however, the rest is defined through the molecular mechanics force field (MMFF). In this review, we have extensively reviewed recent literature pertaining to the use and applications of hybrid QM/MM simulations for ligand and structure-based computational methods for the design and discovery of therapeutic agents.

The Role of Computational Chemistry in Corrosion Inhibition: a Review

The Role of Computational Chemistry in Corrosion Inhibition: a Review

The Role of Computational Chemistry in Discovering and Understanding Organic Photocatalysts for Renewable Fuel Synthesis

In this review the role computational chemistry plays in helping to rationalize the ability of organic materials, such as conjugated polymers, to drive photocatalytic water splitting and CO2 reduction, and the discovery of new organic photocatalysts, is reviewed. The ways in which organic photocatalysts differ from their inorganic counterparts, the mechanism by which such materials, when illuminated, reduce protons or CO2 and oxidize water or sacrificial donors, and how this can be studied using computational methods, as well as the high‐throughput virtual screening of organic materials as photocatalysts, are discussed. Finally, the current opportunities and challenges associated with studying photocatalysts computationally, are examined.

Application of computational chemistry for adsorption studies on metal–organic frameworks used for carbon capture

Abstract Computational chemistry is invaluable in calculating macroscopic and microscopic details of systems application in chemical industries which are involved in carbon capture through precombustion, post-combustion and oxy combustion technologies. This review discusses the role of computational chemistry for adsorption studies of metal–organic frameworks (MOFs) which can be utilized for carbon capture. Principles of quantum mechanics–molecular mechanics are used to devise the electrostatic charges and isotherm parameters on the MOFs. MOFs for carbon capture which can be compatible and which can withstand the severity in chemical industries can be effectively studied using grand canonical Monte Carlo simulation by selecting appropriate force fields. Since flue gases contain a host of other gases in addition to oxides of carbon, capture by MOFs has to be carefully modelled and the software useful for this study are mentioned in this review. The simulated adsorption isotherms should be compared with experimental adsorption isotherms to validate the study. The adsorption model for carbon dioxide adsorption on MOFs is generally reported to be type I reversible isotherm and the kinetics is in good agreement with pseudo-second-order kinetics. Graphical Abstract: Graphical Abstract

The Role of Computational Chemistry in the Experimental Determination of the Dipole Moment of Molecules in Solution.

The methods for the experimental determination of electric dipole moment of molecules in solution from measurements of dielectric permittivity and refractive index are traditionally based on the classical Onsager model. In this model the molecular solute is approximated as a simple polarizable point dipole inside a spherical or ellipsoidal cavity of a dielectric medium representing the solvent. However, the inadequacies of the model resulting from the assumption of a simple shape of the cavity, for the evaluation of the cavity field effect, and from the uncertainty of the polarizability of the molecular solute influences the results and hampers the comparison with the electric dipole moments computed from quantum chemical solvation models. In this article we propose a new method for the experimental determination of the electric dipole moment in solution in which information from the Polarizable Continuum Model calculations are used in place of the Onsager model. The new method overcomes the limitations of this latter model regarding both the cavity field effect and the polarizability of the molecular solutes, and thus allows a coherent comparison between experimental and computed dipole moments of solvated molecules. © 2019 Wiley Periodicals, Inc.

Computational Approaches Towards Kinases as Attractive Targets for Anticancer Drug Discovery and Development.

One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development. Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets. Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer. Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.

Multicomponent Density Functional Theory: Impact of Nuclear Quantum Effects on Proton Affinities and Geometries.

Nuclear quantum effects such as zero point energy play a critical role in computational chemistry and often are included as energetic corrections following geometry optimizations. The nuclear-electronic orbital (NEO) multicomponent density functional theory (DFT) method treats select nuclei, typically protons, quantum mechanically on the same level as the electrons. Electron-proton correlation is highly significant, and inadequate treatments lead to highly overlocalized nuclear densities. A recently developed electron-proton correlation functional, epc17, has been shown to provide accurate nuclear densities for molecular systems. Herein, the NEO-DFT/epc17 method is used to compute the proton affinities for a set of molecules and to examine the role of nuclear quantum effects on the equilibrium geometry of FHF-. The agreement of the computed results with experimental and benchmark values demonstrates the promise of this approach for including nuclear quantum effects in calculations of proton affinities, pKa's, optimized geometries, and reaction paths.

New Mechanistic Insights on the Selectivity of Transition-Metal-Catalyzed Organic Reactions: The Role of Computational Chemistry.

With new advances in theoretical methods and increased computational power, applications of computational chemistry are becoming practical and routine in many fields of chemistry. In organic chemistry, computational chemistry plays an indispensable role in elucidating reaction mechanisms and the origins of various selectivities, such as chemo-, regio-, and stereoselectivities. Consequently, mechanistic understanding improves synthesis and assists in the rational design of new catalysts. In this Account, we present some of our recent works to illustrate how computational chemistry provides new mechanistic insights for improvement of the selectivities of several organic reactions. These examples include not only explanations for the existing experimental observations, but also predictions which were subsequently verified experimentally. This Account consists of three sections discuss three different kinds of selectivities. The first section discusses the regio- and stereoselectivities of hydrosilylations of alkynes, mainly catalyzed by [Cp*Ru(MeCN)3](+) or [CpRu(MeCN)3](+). Calculations suggest a new mechanism that involves a key ruthenacyclopropene intermediate. This mechanism not only explains the unusual Markovnikov regio-selectivity and anti-addition stereoselectivity observed by Trost and co-workers, but also motivated further experimental investigations. New intriguing experimental observations and further theoretical studies led to an extension of the reaction mechanism. The second section includes three cases of meta-selective C-H activation of aryl compounds. In the case of Cu-catalyzed selective meta-C-H activation of aniline, a new mechanism that involves a Cu(III)-Ar-mediated Heck-like transition state, in which the Ar group acts as an electrophile, was proposed. This mechanism predicted a higher reactivity for more electron-deficient Ar groups, which was supported by experiments. For two template-mediated, meta-selective C-H bond activations catalyzed by Pd(II), different mechanisms were derived for the two templates. One involves a dimeric Pd-Pd or Pd-Ag active catalyst, and the other involves a monomeric Pd catalyst, in which a monoprotected amino acid coordinates in a bidentate fashion and serves as an internal base for C-H activation. The third section discusses a desymmetry strategy in asymmetric synthesis. The construction of rigid skeletons is critical for these catalysts to distinguish two prochiral groups. Overall, fruitful collaborations between computational and experimental chemists have provided new and comprehensive mechanistic understanding and insights into these useful reactions.

LONG-RANGE VAN DER WAALS INTERACTION

Van der Waals interaction is an elusive many-body effect arising from instantaneous charge fluctuations. Fundamental understanding of this effect plays an important role in computational chemistry, physics and materials science. In this article, recent advances in the evaluation of van der Waals coefficients, in particular the higher-order ones, are reviewed.

Remarkable diversity of carbon–carbon bonds: structures and properties of fullerenes, carbon nanotubes, and graphene

Large scientific community has been passionate in understanding different carbon nanostructures for last two decades. In this review, we present the general description of low-dimensional carbon allotropes such as fullerenes (0D), carbon nanotubes (1D), and graphene (2D). These structures have unique diversity of carbon–carbon bonds. Structures and electronic properties of fullerenes, small closed carbon cages, and giant fullerenes are illustrated. We point out the complexity in the area of fullerene research because of a wide range of structures and number of possible isomers of fullerenes. The concept of isolated pentagon rule in fullerenes is highlighted. We delineate the usefulness of pyramidalization angle in evaluating the curvature of fullerenes. The role of computational chemistry in identifying different isomers of fullerenes and validating the experimental results in ambiguous situations is also briefly mentioned. Properties of different types of carbon nanotubes, particularly single-walled carbon nanotubes (SWCNTs) and their structural features are summarized. The use of pyramidalization angle (θP) and π-orbital misalignment angles in predicting the reactivity of different carbon atom sites of SWCNTs is discussed. Finally, we outline the structures and electronic properties of graphene, and discuss the status of experimental investigations of this species.

Role of computational chemistry in support of hazard identification (ID): mechanism-based SARs

A mechanism-based structure-activity relationship (SAR) study examines the structural basis for a chemical/biological activity by targeting a single or a few stages in a postulated mechanism of action. Computational chemistry approaches provide a valuable complement to experiment for probing such associations, but require a highly focused viewpoint that neglects much of the full biological and chemical interaction problem. Research questions are formulated in terms of fundamental structure and reactivity properties and are designed to test key assumptions of a postulated mechanism of activity. The results of such studies can aid in the generation of new hypotheses, suggest new experiments, and provide scientific rationale for extrapolation in hazard identification (ID). Toxicologists and computational chemists bring very different, yet complementary viewpoints, approaches, and expertise to bear on the hazard ID problem. However, improved communication and interaction between these two groups is needed to most productively address hazard ID issues.