scholarly journals The quantum mechanics-based polarizable force field for water simulations

2018 ◽  
Vol 149 (17) ◽  
pp. 174502 ◽  
Author(s):  
Saber Naserifar ◽  
William A. Goddard
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Polarizable Force Field

scholarly journals Polarizable Force Field for CO2in M-MOF-74 Derived from Quantum Mechanics

2018 ◽  
Vol 122 (42) ◽  
pp. 24488-24498 ◽  
Author(s):  
Tim M. Becker ◽  
Li-Chiang Lin ◽  
David Dubbeldam ◽  
Thijs J. H. Vlugt
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Polarizable Force Field

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>

Modelling Flexible Protein-Ligand Binding in p38α MAP Kinase using the QUBE Force Field

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Map Kinase ◽  
Binding Free Energy ◽  
Quantum Mechanical ◽  
Enhanced Sampling ◽  
Relative Binding ◽  
Stringent Test ◽  
Flexible Protein ◽  
P38α Map Kinase

<div><div><div><p>The quantum mechanical bespoke (QUBE) force field is used to retrospectively calculate the relative binding free energy of a series of 17 flexible inhibitors of p38α MAP kinase. The size and flexibility of the chosen molecules represent a stringent test of the derivation of force field parameters from quantum mechanics, and enhanced sampling is required to reduce the dependence of the results on the starting structure. Competitive accuracy with a widely-used biological force field is achieved, indicating that quantum mechanics derived force fields are approaching the accuracy required to provide guidance in prospective drug discovery campaigns.</p></div></div></div>

Parametrizing a polarizable force field from ab initio data. I. The fluctuating point charge model

1999 ◽  
Vol 110 (2) ◽  
pp. 741-754 ◽  
Author(s):  
Jay L. Banks ◽  
George A. Kaminski ◽  
Ruhong Zhou ◽  
Daniel T. Mainz ◽  
B. J. Berne ◽  
...  
Keyword(s):  
Force Field ◽  
Ab Initio ◽  
Point Charge ◽  
Point Charge Model ◽  
Polarizable Force Field ◽  
Charge Model

Molecular Dynamics Study of Hydration in Ethanol−Water Mixtures Using a Polarizable Force Field†

2005 ◽  
Vol 109 (14) ◽  
pp. 6705-6713 ◽  
Author(s):  
Sergei Yu. Noskov ◽  
Guillaume Lamoureux ◽  
Benoît Roux
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Polarizable Force Field

DFT-based polarizable force field for TiO2and SiO2

2015 ◽  
Vol 23 (7) ◽  
pp. 074005 ◽  
Author(s):  
Dario Corradini ◽  
Yoshiki Ishii ◽  
Norikazu Ohtori ◽  
Mathieu Salanne
Keyword(s):  
Force Field ◽  
Polarizable Force Field
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