Molecular mechanics force-field development for amino acid zwitterions

2003 ◽  
Vol 24 (1) ◽  
pp. 111-128 ◽  
Author(s):  
K. N. Kirschner ◽  
A. H. Lewin ◽  
J. P. Bowen
Keyword(s):  
Amino Acid ◽  
Force Field ◽  
Molecular Mechanics ◽  
Field Development ◽  
Force Field Development

Molecular Mechanics Force Field Development and Applications

ChemInform ◽  
2003 ◽  
Vol 34 (19) ◽  
Author(s):  
Pat Metthe Todebush ◽  
J. Phillip Bowen
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Field Development ◽  
Force Field Development

Molecular mechanics (MM4) force field development for phosphine and its alkyl derivatives

Chirality ◽  
2002 ◽  
Vol 14 (2-3) ◽  
pp. 220-231 ◽  
Author(s):  
Patricia Metthe Todebush ◽  
Guyan Liang ◽  
J. Phillip Bowen
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Field Development ◽  
Force Field Development ◽  
Alkyl Derivatives

scholarly journals Exploration and Validation of Force Field Design Protocols through QM-to-MM Mapping

2021 ◽  
Author(s):  
Chris Ringrose ◽  
Joshua Horton ◽  
Lee-Ping Wang ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Molecular Mechanics ◽  
Organic Molecule ◽  
Force Fields ◽  
Small Organic Molecule ◽  
Field Development ◽  
Force Field Development ◽  
Fitting Parameters ◽  
Made In

The scale of the parameter optimisation problem in traditional molecular mechanics force field construction means that design of a new force field is a long process, and sub-optimal choices made in the early stages can persist for many generations of the force field. We hypothesise that careful use of quantum mechanics to inform molecular mechanics parameter derivation (QM-to-MM mapping) should be used to significantly reduce the number of parameters that require fitting to experiment and increase the pace of force field development. Here, we design a collection of 15 new protocols for small, organic molecule force field design, and test their accuracy against experimental liquid properties. Our best performing model has only seven fitting parameters, yet achieves mean unsigned errors of just 0.031 g/cm3 and 0.69 kcal/mol in liquid densities and heats of vaporisation, compared to experiment. The software required to derive the designed force fields is freely available at https://github.com/qubekit/QUBEKit.

Driving Torsion Scans with Wavefront Propagation

2020 ◽  
Author(s):  
Yudong Qiu ◽  
Daniel Smith ◽  
Chaya Stern ◽  
mudong feng ◽  
Lee-Ping Wang
Keyword(s):  
Potential Energy ◽  
Force Field ◽  
Degrees Of Freedom ◽  
Computational Cost ◽  
Initial Guess ◽  
Field Development ◽  
Force Field Development ◽  
Wavefront Propagation ◽  
Open Source Software Package

<div>The parameterization of torsional / dihedral angle potential energy terms is a crucial part of developing molecular mechanics force fields.</div><div>Quantum mechanical (QM) methods are often used to provide samples of the potential energy surface (PES) for fitting the empirical parameters in these force field terms.</div><div>To ensure that the sampled molecular configurations are thermodynamically feasible, constrained QM geometry optimizations are typically carried out, which relax the orthogonal degrees of freedom while fixing the target torsion angle(s) on a grid of values.</div><div>However, the quality of results and computational cost are affected by various factors on a non-trivial PES, such as dependence on the chosen scan direction and the lack of efficient approaches to integrate results started from multiple initial guesses.</div><div>In this paper we propose a systematic and versatile workflow called \textit{TorsionDrive} to generate energy-minimized structures on a grid of torsion constraints by means of a recursive wavefront propagation algorithm, which resolves the deficiencies of conventional scanning approaches and generates higher quality QM data for force field development.</div><div>The capabilities of our method are presented for multi-dimensional scans and multiple initial guess structures, and an integration with the MolSSI QCArchive distributed computing ecosystem is described.</div><div>The method is implemented in an open-source software package that is compatible with many QM software packages and energy minimization codes.</div>

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