scholarly journals A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules

1995 ◽  
Vol 117 (19) ◽  
pp. 5179-5197 ◽  
Author(s):  
Wendy D. Cornell ◽  
Piotr Cieplak ◽  
Christopher I. Bayly ◽  
Ian R. Gould ◽  
Kenneth M. Merz ◽  
...  
Keyword(s):  
Nucleic Acids ◽  
Force Field ◽  
Second Generation ◽  
Organic Molecules

scholarly journals A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic MoleculesJ.Am.Chem.Soc.1995,117, 5179−5197

1996 ◽  
Vol 118 (9) ◽  
pp. 2309-2309 ◽  
Author(s):  
Wendy D. Cornell ◽  
Piotr Cieplak ◽  
Christopher I. Bayly ◽  
Ian R. Gould ◽  
Kenneth M. Merz ◽  
...  
Keyword(s):  
Nucleic Acids ◽  
Force Field ◽  
Second Generation

Hydration Free Energies of Organic Molecules in the FreeSolv Database Calculated with Polarized Atom In Molecules Atomic Charges and the GAFF Force Field.

2018 ◽  
Author(s):  
Maximiliano Riquelme ◽  
Alejandro Lara ◽  
David L. Mobley ◽  
Toon Vestraelen ◽  
Adelio R Matamala ◽  
...  
Keyword(s):  
Force Field ◽  
Functional Groups ◽  
Electrostatic Interactions ◽  
Organic Molecules ◽  
Force Fields ◽  
Chemical Elements ◽  
Atomic Charges ◽  
Free Energies ◽  
Molecular Systems ◽  
Solvent Model

<div>Computer simulations of bio-molecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in bio-molecular systems and are therein described by atomic point charges.</div><div>In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute's electron density computed with an implicit solvent model and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the MBIS atomic charge method, including the solvent polarization, with a root mean square error of 2.0 kcal mol<sup>-1</sup> for the 613 organic molecules studied. The largest deviation was observed for phosphor-containing molecules and the molecules with amide, ester and amine functional groups.</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>

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>

Calibrating nucleic acids torsional energetics in force-field: insights from model compounds

2001 ◽  
Vol 537 (1-3) ◽  
pp. 283-305 ◽  
Author(s):  
D Bosch ◽  
N Foloppe ◽  
N Pastor ◽  
L Pardo ◽  
M Campillo
Keyword(s):  
Nucleic Acids ◽  
Force Field ◽  
Model Compounds

scholarly journals 110 Refinement of force field torsion parameters for nucleic acids based on inclusion of conformation-dependent solvation effects

2013 ◽  
Vol 31 (sup1) ◽  
pp. 70-70 ◽  
Author(s):  
Marie Zgarbová ◽  
Michal Otyepka ◽  
Pavel Banáš ◽  
F. Javier Luque ◽  
Thomas E. Cheatham ◽  
...  
Keyword(s):  
Nucleic Acids ◽  
Force Field ◽  
Solvation Effects

scholarly journals Refinement of the Cornell et al. Nucleic Acids Force Field Based on Reference Quantum Chemical Calculations of Glycosidic Torsion Profiles

2011 ◽  
Vol 7 (9) ◽  
pp. 2886-2902 ◽  
Author(s):  
Marie Zgarbová ◽  
Michal Otyepka ◽  
Jiří Šponer ◽  
Arnošt Mládek ◽  
Pavel Banáš ◽  
...  
Keyword(s):  
Nucleic Acids ◽  
Force Field ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical
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