scholarly journals Evaluation of non-covalent interactions of chlorambucil (monomer and dimer) and its interaction with biological targets: Vibrational frequency shift, electron density topological and automated docking analysis

2018 ◽  
Vol 11 (5) ◽  
pp. 591-608 ◽  
Author(s):  
T. Karthick ◽  
Poonam Tandon ◽  
Karnica Srivastava ◽  
Swapnil Singh
Keyword(s):  
Frequency Shift ◽  
Electron Density ◽  
Vibrational Frequency ◽  
Docking Analysis ◽  
Biological Targets ◽  
Non Covalent Interactions ◽  
Automated Docking ◽  
Covalent Interactions

scholarly journals Understanding the Reactivity of Trimethylsilyldiazoalkanes Participating in [3+2] Cycloaddition Reactions towards Diethylfumarate with a Molecular Electron Density Theory Perspective

Organics ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 3-18
Author(s):  
Luis R. Domingo ◽  
Nivedita Acharjee ◽  
Haydar A. Mohammad-Salim
Keyword(s):  
Electron Density ◽  
Energy Cost ◽  
Trimethylsilyl Group ◽  
Gibbs Energies ◽  
Molecular Electron ◽  
One Step ◽  
Molecular Electron Density Theory ◽  
Non Covalent Interactions ◽  
Covalent Interactions ◽  
Step Mechanism

A Molecular Electron Density Theory (MEDT) study is presented here for [3+2] cycloaddition (32CA) reactions of three trimethylsilyldiazoalkanes with diethyl fumarate. The presence of silicon bonded to the carbon of these silyldiazoalkanes changes its structure and reactivity from a pseudomonoradical to that of a zwitterionic one. A one-step mechanism is predicted for these polar zw-type 32CA reactions with activation enthalpies in CCl4 between 8.0 and 19.7 kcal·mol−1 at the MPWB1K (PCM)/6-311G(d,p) level of theory. The negative reaction Gibbs energies between −3.1 and −13.2 kcal·mole−1 in CCl4 suggests exergonic character, making the reactions irreversible. Analysis of the sequential changes in the bonding pattern along the reaction paths characterizes these zw-type 32CA reactions. The increase in nucleophilic character of the trimethylsilyldiazoalkanes makes these 32CA reactions more polar. Consequently, the activation enthalpies are decreased and the TSs require less energy cost. Non-covalent interactions at the TSs account for the stereoselectivity found in these 32CA reactions involving the bulky trimethylsilyl group.

Electron density of a benzoylated tetrafructopyranose

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Peter Luger ◽  
Birger Dittrich
Keyword(s):  
Electron Density ◽  
X Ray Diffraction ◽  
X Ray ◽  
Atomic Properties ◽  
Phenyl Rings ◽  
Structural Database ◽  
Non Covalent Interactions ◽  
Weak Surface ◽  
Average Bond ◽  
Covalent Interactions

Abstract The electron density distribution (EDD) of a tetrasaccharide composed of four benzoylated fructopyranosyl units was obtained by refinement with scattering factors from the invariom library. X-ray diffraction data was downloaded from the Cambridge Structural Database (CSD). Bond topological and atomic properties were obtained by application of Bader’s QTAIM formalism. From a large number of 105 C–C bonds in the molecule average bond orders for 33 single and 72 aromatic bonds were calculated yielding values of 1.33 and 1.61. Molecular Hirshfeld and electrostatic potential (ESP) surfaces show that only weak non-covalent interactions exist. The phenyl rings of the benzoyl fragments in the outer regions of the molecule generate a positive ESP shell with repulsive properties between adjacent molecules. Weak surface interactions result in a rather unusual low density around 1.3 g cm−3, which is understandable when compared to other carbohydrates where strong O–H⋯O hydrogen bonds allow a 20% more dense packing with densities >1.5 g cm−3 as determined by single crystal X-ray diffraction.

Insights into Non-Covalent Interactions with a Machine-Learned Electron Density

2019 ◽  
Author(s):  
Alberto Fabrizio ◽  
Andrea Grisafi ◽  
benjamin meyer ◽  
Michele Certiotti ◽  
Clemence Corminboeuf
Keyword(s):  
Electron Density ◽  
Real Space ◽  
Total Electron Density ◽  
Total Electron ◽  
Chemical Systems ◽  
Machine Learning Model ◽  
Non Covalent Interactions ◽  
Diverse Ensemble ◽  
Covalent Interactions ◽  
Atomic Coordinates

<div>Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. </div><div>The total electron density rho(r) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrodinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or for very large chemical systems, such as peptides and proteins. </div><div>Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying rho(r) in a diverse ensemble of sidechain-sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides.</div>

Unraveling non-covalent interactions within flexible biomolecules: from electron density topology to gas phase spectroscopy

2014 ◽  
Vol 16 (21) ◽  
pp. 9876 ◽  
Author(s):  
R. Chaudret ◽  
B. de Courcy ◽  
J. Contreras-García ◽  
E. Gloaguen ◽  
A. Zehnacker-Rentien ◽  
...  
Keyword(s):  
Gas Phase ◽  
Electron Density ◽  
Electron Density Topology ◽  
Density Topology ◽  
Non Covalent Interactions ◽  
Gas Phase Spectroscopy ◽  
Covalent Interactions

Experimental electron density study of the supramolecular aggregation between 4,4′-dipyridyl-N,N′-dioxide and 1,4-diiodotetrafluorobenzene at 90 K

2004 ◽  
Vol 60 (5) ◽  
pp. 559-568 ◽  
Author(s):  
Riccardo Bianchi ◽  
Alessandra Forni ◽  
Tullio Pilati
Keyword(s):  
Electron Density ◽  
Halogen Bond ◽  
X Ray Diffraction ◽  
One Dimensional ◽  
X Ray ◽  
Non Covalent Interactions ◽  
Covalent Interactions ◽  
Experimental Electron Density ◽  
Supramolecular Aggregation ◽  
Density Study

The electron density of the halogen-bonded complex of 4,4′-dipyridyl-N,N′-dioxide (bpNO) with 1,4-diiodotetrafluorobenzene (F4dIb) at 90 K has been determined by X-ray diffraction and analysed. The nature of the I...O intermolecular bond connecting the bpNO and F4dIb molecules into one-dimensional infinite chains, as well as the other non-covalent interactions present in the crystal, such as C—H...O, C—H...F and C—H...I hydrogen bonds and C...C, C...N, C...I and F...F interactions, have been investigated. The integration of electron density over the atomic basins reveals the electrostatic nature of the I...O halogen bond, which is very similar to a previously analysed I...N halogen bond.

scholarly journals Insights into Non-Covalent Interactions with a Machine-Learned Electron Density

2019 ◽  
Author(s):  
Alberto Fabrizio ◽  
Andrea Grisafi ◽  
benjamin meyer ◽  
Michele Certiotti ◽  
Clemence Corminboeuf
Keyword(s):  
Electron Density ◽  
Real Space ◽  
Total Electron Density ◽  
Total Electron ◽  
Chemical Systems ◽  
Machine Learning Model ◽  
Non Covalent Interactions ◽  
Diverse Ensemble ◽  
Covalent Interactions ◽  
Atomic Coordinates

<div>Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. </div><div>The total electron density rho(r) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrodinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or for very large chemical systems, such as peptides and proteins. </div><div>Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying rho(r) in a diverse ensemble of sidechain-sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides.</div>

scholarly journals Synthesis, Crystal Structure, and Computational Methods of Vanadium and Copper Compounds as Potential Drugs for Cancer Treatment

Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4679
Author(s):  
Nidia D. Corona-Motolinia ◽  
Beatriz Martínez-Valencia ◽  
Lisset Noriega ◽  
Brenda L. Sánchez-Gaytán ◽  
Miguel Ángel Méndez-Rojas ◽  
...  
Keyword(s):  
Density Functional ◽  
Basis Set ◽  
Cancer Therapies ◽  
X Ray Diffraction ◽  
Biological Targets ◽  
Copper Compounds ◽  
Supramolecular Arrangements ◽  
Structural And Electronic Properties ◽  
Non Covalent Interactions ◽  
Covalent Interactions

Transition metal-based compounds have shown promising uses as therapeutic agents. Among their unique characteristics, these compounds are suitable for interaction with specific biological targets, making them important potential drugs to treat various diseases. Copper compounds, of which Casiopeinas® are an excellent example, have shown promising results as alternatives to current cancer therapies, in part because of their intercalative properties with DNA. Vanadium compounds have been extensively studied for their pharmacological properties and application, mostly in diabetes, although recently, there is a growing interest in testing their activity as anti-cancer agents. In the present work, two compounds, [Cu(Metf)(bipy)Cl]Cl·2H2O and [Cu(Impy)(Gly)(H2O)]VO3, were obtained and characterized by visible and FTIR spectroscopies, single-crystal X-ray diffraction, and theoretical methods. The structural and electronic properties of the compounds were calculated through the density functional theory (DFT) using the Austin–Frisch–Petersson functional with dispersion APFD, and the 6-311 + G(2d,p) basis set. Non-covalent interactions were analyzed using Hirshfeld surface analysis (HSA) and atom in molecules analysis (AIM). Additionally, docking analysis to test DNA/RNA interactions with the Casiopeina-like complexes were carried out. The compounds provide metals that can interact with critical biological targets. In addition, they show interesting non-covalent interactions that are responsible for their supramolecular arrangements.

Testing the tools for revealing and characterizing the iodine–iodine halogen bond in crystals

2017 ◽  
Vol 73 (2) ◽  
pp. 217-226 ◽  
Author(s):  
Ekaterina Bartashevich ◽  
Irina Yushina ◽  
Kristina Kropotina ◽  
Svetlana Muhitdinova ◽  
Vladimir Tsirelson
Keyword(s):  
Electron Density ◽  
Halogen Bond ◽  
Electron Shell ◽  
Electron Localization Function ◽  
Electron Localization ◽  
Localization Function ◽  
Non Covalent Interactions ◽  
The One ◽  
Covalent Interactions

To understand what tools are really suitable to identify and classify the iodine–iodine non-covalent interactions in solid organic polyiodides, we have examined the anisotropy of the electron density within the iodine atomic basin along and across the iodine–iodine halogen bond using the Laplacian of electron density, one-electron potential and electron localization function produced by Kohn–Sham calculations with periodic boundary conditions. The Laplacian of electron density exhibits the smallest anisotropy and yields a vague picture of the outermost electronic shells. The one-electron potential does not show such a deficiency and reveals that the valence electron shell for the halogen-bond acceptor iodine is always wider than that for the halogen-bond donor iodine along its σ-hole direction. We have concluded that the one-electron potential is the most suitable for classification of the iodine–iodine bonds and interactions in complicated cases, while the electron localization function allows to distinguish the diiodine molecule bonded with the monoiodide anion from the typical triiodide anion.

scholarly journals Strength of the [Z–I···Hal]− and [Z–Hal···I]− Halogen Bonds: Electron Density Properties and Halogen Bond Length as Estimators of Interaction Energy

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 2083
Author(s):  
Maxim L. Kuznetsov
Keyword(s):  
Bond Length ◽  
Interaction Energy ◽  
Electron Density ◽  
Halogen Bond ◽  
Bond Critical Point ◽  
Halogen Bonds ◽  
Curvature Potential ◽  
Non Covalent Interactions ◽  
Covalent Interactions

Bond energy is the main characteristic of chemical bonds in general and of non-covalent interactions in particular. Simple methods of express estimates of the interaction energy, Eint, using relationships between Eint and a property which is easily accessible from experiment is of great importance for the characterization of non-covalent interactions. In this work, practically important relationships between Eint and electron density, its Laplacian, curvature, potential, kinetic, and total energy densities at the bond critical point as well as bond length were derived for the structures of the [Z–I···Hal]– and [Z–Hal···I]– types bearing halogen bonds and involving iodine as interacting atom(s) (totally 412 structures). The mean absolute deviations for the correlations found were 2.06–4.76 kcal/mol.

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