Hydrogen Abstraction by Chlorine Atom from Small Organic Molecules Containing Amino Acid Functionalities: An Assessment of Theoretical Procedures†

2009 ◽  
Vol 113 (43) ◽  
pp. 11817-11832 ◽  
Author(s):  
Mark S. Taylor ◽  
Sandra A. Ivanic ◽  
Geoffrey P. F. Wood ◽  
Christopher J. Easton ◽  
George B. Bacskay ◽  
...  
Keyword(s):  
Amino Acid ◽  
Chlorine Atom ◽  
Organic Molecules ◽  
Hydrogen Abstraction ◽  
Small Organic Molecules

scholarly journals Importance of Chiral Recognition in Designing Metal-Free Ligands for G-Quadruplex DNA

Molecules ◽  
2019 ◽  
Vol 24 (8) ◽  
pp. 1473 ◽  
Author(s):  
Dora M. Răsădean ◽  
Samuel W. O. Harrison ◽  
Isobel R. Owens ◽  
Aucéanne Miramont ◽  
Frances M. Bromley ◽  
...  
Keyword(s):  
Amino Acid ◽  
Chiral Recognition ◽  
Organic Molecules ◽  
Small Organic Molecules ◽  
Quadruplex Dna ◽  
Metal Free ◽  
G Quadruplex ◽  
Dna Stabilization

Four pairs of amino acid-functionalized naphthalenediimide enantiomers (d- and l-lysine derived NDIs) were screened toward G-quadruplex forming sequences in telomeres (h-TELO) and oncogene promoters: c-KIT1, c-KIT2, k-RAS and BCL-2. This is the first study to address the effect of point chirality toward G-quadruplex DNA stabilization using purely small organic molecules. Enantioselective behavior toward the majority of ligands was observed, particularly in the case of parallel conformations of c-KIT2 and k-RAS. Additionally, Nε-Boc-l-Lys-NDI and Nε-Boc-d-Lys-NDI discriminate between quadruplexes with parallel and hybrid topologies, which has not previously been observed with enantiomeric ligands.

scholarly journals Prediction of Homolytic Bond Dissociation Enthalpies for Organic Molecules at near Chemical Accuracy with Sub-Second Computational Cost

2019 ◽  
Author(s):  
Peter St. John ◽  
Yanfei Guan ◽  
Yeonjoon Kim ◽  
Seonah Kim ◽  
Robert Paton
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Soot Formation ◽  
Chemical Reactivity ◽  
Hydrogen Abstraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Functional Theory ◽  
Small Organic Molecules ◽  
Bond Dissociation

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity. However, BDE computations at sufficiently high levels of quantum mechanical (QM) theory require substantial computing resources. We have therefore developed A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET), capable of accurately predicting BDEs for organic molecules in a fraction of a second. Automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory were performed for 42,577 small organic molecules, resulting in a dataset of 290,664 BDEs. A graph neural network was trained on a subset of these results, achieving a mean absolute error of 0.58 kcal/mol for the BDE values of unseen molecules. An interface for the developed prediction tool is available online at https://ml.nrel.gov/bde. The model rapidly and accurately predicts major sites of hydrogen abstraction in metabolism of drug-like molecules and determines the dominant molecular fragmentation pathways during soot formation.

Complexation of Organic Dyes by Peptides Built of Lysine and Glutamic Acid Amides

2002 ◽  
Vol 57 (3) ◽  
pp. 343-348 ◽  
Author(s):  
Heinz Mehlmann ◽  
Daniel Olschewski ◽  
Andrey Olschewski ◽  
Martin Feigel
Keyword(s):  
Amino Acid ◽  
Glutamic Acid ◽  
Crystal Violet ◽  
Organic Molecules ◽  
Organic Dyes ◽  
Amino Acid Derivatives ◽  
Small Organic Molecules ◽  
Protected Amino Acid ◽  
Artificial Receptors ◽  
Complex Organic

AbstractTwo amide libraries, Fmoc-[Lys(aci)]4-Gly-resin (1) (aci = 2-naphthylcarbonyl, 1-adamantylcarbonyl and benzyloxycarbonyl) and Fmoc-[δ-Glu(α-amidei)]4-Gly-resin (2) (amidei = morpholineamide, piperidineamide, (N´-phenyl)-piperazineamide), have been synthesized from the corresponding Fmoc-protected amino acid derivatives. Beads of the libraries complex organic dyes (crystal violet and Sudan black) differently according to the sequence of residues in 1 or 2. The results are considered a step towards artificial receptors for small organic molecules build from linear oligoamides.

scholarly journals Prediction of Homolytic Bond Dissociation Enthalpies for Organic Molecules at near Chemical Accuracy with Sub-Second Computational Cost

2019 ◽  
Author(s):  
Peter St. John ◽  
Yanfei Guan ◽  
Yeonjoon Kim ◽  
Seonah Kim ◽  
Robert Paton
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Soot Formation ◽  
Chemical Reactivity ◽  
Hydrogen Abstraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Functional Theory ◽  
Small Organic Molecules ◽  
Bond Dissociation

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity. However, BDE computations at sufficiently high levels of quantum mechanical (QM) theory require substantial computing resources. We have therefore developed A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET), capable of accurately predicting BDEs for organic molecules in a fraction of a second. Automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory were performed for 42,577 small organic molecules, resulting in a dataset of 290,664 BDEs. A graph neural network was trained on a subset of these results, achieving a mean absolute error of 0.58 kcal/mol for the BDE values of unseen molecules. An interface for the developed prediction tool is available online at https://ml.nrel.gov/bde. The model rapidly and accurately predicts major sites of hydrogen abstraction in metabolism of drug-like molecules and determines the dominant molecular fragmentation pathways during soot formation.

scholarly journals Prediction of Homolytic Bond Dissociation Enthalpies for Organic Molecules at near Chemical Accuracy with Sub-Second Computational Cost

2019 ◽  
Author(s):  
Peter St. John ◽  
Yanfei Guan ◽  
Yeonjoon Kim ◽  
Seonah Kim ◽  
Robert Paton
Keyword(s):  
Density Functional ◽  
Organic Molecules ◽  
Soot Formation ◽  
Chemical Reactivity ◽  
Hydrogen Abstraction ◽  
Computational Cost ◽  
Absolute Error ◽  
Functional Theory ◽  
Small Organic Molecules ◽  
Bond Dissociation

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity. However, BDE computations at sufficiently high levels of quantum mechanical (QM) theory require substantial computing resources. We have therefore developed A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET), capable of accurately predicting BDEs for organic molecules in a fraction of a second. Automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory were performed for 42,577 small organic molecules, resulting in a dataset of 290,664 BDEs. A graph neural network was trained on a subset of these results, achieving a mean absolute error of 0.58 kcal/mol for the BDE values of unseen molecules. An interface for the developed prediction tool is available online at https://ml.nrel.gov/bde. The model rapidly and accurately predicts major sites of hydrogen abstraction in metabolism of drug-like molecules and determines the dominant molecular fragmentation pathways during soot formation.

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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