Derivation of Class II Force Fields. 7. Nonbonded Force Field Parameters for Organic Compounds

1999 ◽  
Vol 103 (33) ◽  
pp. 6998-7014 ◽  
Author(s):  
Carl S. Ewig ◽  
Thomas S. Thacher ◽  
Arnold T. Hagler
Keyword(s):  
Force Field ◽  
Organic Compounds ◽  
Force Fields ◽  
Class Ii ◽  
Force Field Parameters

Derivation of class II force fields. VIII. Derivation of a general quantum mechanical force field for organic compounds

2001 ◽  
Vol 22 (15) ◽  
pp. 1782-1800 ◽  
Author(s):  
Carl S. Ewig ◽  
Rajiv Berry ◽  
Uri Dinur ◽  
Jörg-Rüdiger Hill ◽  
Ming-Jing Hwang ◽  
...  
Keyword(s):  
Force Field ◽  
Organic Compounds ◽  
Force Fields ◽  
Class Ii ◽  
Mechanical Force ◽  
Quantum Mechanical ◽  
General Quantum

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>

PROTEIN FORCE-FIELD PARAMETERS OPTIMIZED WITH THE PROTEIN DATA BANK I: FORCE-FIELD OPTIMIZATIONS

2004 ◽  
Vol 03 (03) ◽  
pp. 339-358 ◽  
Author(s):  
YOSHITAKE SAKAE ◽  
YUKO OKAMOTO
Keyword(s):  
Force Field ◽  
Protein Data Bank ◽  
Force Fields ◽  
Data Bank ◽  
Partial Charge ◽  
Energy Term ◽  
Energy Parameters ◽  
Torsion Energy ◽  
Force Field Parameters

We optimized five existing sets of force-field parameters for protein systems by our recently proposed method. The five force fields are AMBER parm94, AMBER parm96, AMBER parm99, CHARMM version 22, and OPLS-AA. The method consists of minimizing the sum of the square of the force acting on each atom in the proteins with the structures from the Protein Data Bank (PDB). We selected the partial-charge and backbone torsion-energy parameters for this optimization, and 100 molecules from the PDB were used. We gave detailed comparisons of the optimized force fields and found that there is a tendency of convergence towards the same function for the torsion-energy term.

PROTEIN FORCE-FIELD PARAMETERS OPTIMIZED WITH THE PROTEIN DATA BANK II: COMPARISONS OF FORCE FIELDS BY FOLDING SIMULATIONS OF SHORT PEPTIDES

2004 ◽  
Vol 03 (03) ◽  
pp. 359-378 ◽  
Author(s):  
YOSHITAKE SAKAE ◽  
YUKO OKAMOTO
Keyword(s):  
Force Field ◽  
Protein Data Bank ◽  
Force Fields ◽  
Data Bank ◽  
Optimization Method ◽  
Short Peptides ◽  
Force Field Parameters ◽  
Hairpin Structures ◽  
Hairpin Peptides ◽  
Folding Simulations

In Paper I of this series, the formulations of the optimization method of existing force-field parameters for protein systems have been presented. We then applied it to five sets of force-field parameters, namely, AMBER parm94, AMBER parm96, AMBER parm99, CHARMM version 22, and OPLS-AA. In order to test the validity of these force fields, the folding simulations of α-helical and β-hairpin peptides have been performed with each of the original and optimized force-field parameters. We found that all five modified force-field parameters gave both α-helical and β-hairpin structures more consistent with the experimental implications than the original force fields.

Derivation of Class II Force Fields. III. Characterization of a Quantum Force Field for Alkanes

1994 ◽  
Vol 34 (2) ◽  
pp. 195-231 ◽  
Author(s):  
J.R. Maple ◽  
M.-J. Hwang ◽  
T.P. Stockfisch ◽  
A.T. Hagler
Keyword(s):  
Force Field ◽  
Force Fields ◽  
Class Ii ◽  
Quantum Force

Derivation of class II force fields: V. Quantum force field for amides, peptides, and related compounds

1998 ◽  
Vol 19 (4) ◽  
pp. 430-458 ◽  
Author(s):  
J. R. Maple ◽  
M.-J. Hwang ◽  
K. J. Jalkanen ◽  
T. P. Stockfisch ◽  
A. T. Hagler
Keyword(s):  
Force Field ◽  
Force Fields ◽  
Class Ii ◽  
Quantum Force ◽  
Related Compounds
Sign in / Sign up

Export Citation Format

Share Document