scholarly journals Developing accurate molecular mechanics force fields for conjugated molecular systems

2015 ◽  
Vol 17 (38) ◽  
pp. 25123-25132 ◽  
Author(s):  
Hainam Do ◽  
Alessandro Troisi
Keyword(s):  
Conjugated Polymers ◽  
Molecular Mechanics ◽  
Rapid Method ◽  
Force Fields ◽  
Molecular Systems ◽  
Molecular Semiconductor

A rapid method to parameterize the intramolecular component of classical force fields is proposed and applied to a molecular semiconductor, oligomers of conjugated polymers and a biological chromophore.

scholarly journals Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

2018 ◽  
Author(s):  
David L. Mobley ◽  
Caitlin C. Bannan ◽  
Andrea Rizzi ◽  
Christopher I. Bayly ◽  
John D. Chodera ◽  
...  
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Small Molecule ◽  
Force Fields ◽  
Chemical Space ◽  
Dielectric Constants ◽  
Intermediate Step ◽  
Amber Force Field ◽  
Molecular Systems ◽  
Chemical Perception

AbstractHere, we focus on testing and improving force fields for molecular modeling, which see widespread use in diverse areas of computational chemistry and biomolecular simulation. A key issue affecting the accuracy and transferrability of these force fields is the use of atom typing. Traditional approaches to defining molecular mechanics force fields must encode, within a discrete set of atom types, all information which will ever be needed about the chemical environment; parameters are then assigned by looking up combinations of these atom types in tables. This atom typing approach leads to a wide variety of problems such as inextensible atom-typing machinery, enormous difficulty in expanding parameters encoded by atom types, and unnecessarily proliferation of encoded parameters. Here, we describe a new approach to assigning parameters for molecular mechanics force fields based on the industry standard SMARTS chemical perception language (with extensions to identify specific atoms available in SMIRKS). In this approach, each force field term (bonds, angles, and torsions, and nonbonded interactions) features separate definitions assigned in a hierarchical manner without using atom types. We accomplish this using direct chemical perception, where parameters are assigned directly based on substructure queries operating on the molecule(s) being parameterized, thereby avoiding the intermediate step of assigning atom types — a step which can be considered indirect chemical perception. Direct chemical perception allows for substantial simplification of force fields, as well as additional generality in the substructure queries. This approach is applicable to a wide variety of (bio)molecular systems, and can greatly reduce the number of parameters needed to create a complete force field. Further flexibility can also be gained by allowing force field terms to be interpolated based on the assignment of fractional bond orders via the same procedure used to assign partial charges. As an example of the utility of this approach, we provide a minimalist small molecule force field derived from Merck’s parm@Frosst (an Amber parm99 descendant), in which a parameter definition file only ≈ 300 lines long can parameterize a large and diverse spectrum of pharmaceutically relevant small molecule chemical space. We benchmark this minimalist force field on the FreeSolv small molecule hydration free energy set and calculations of densities and dielectric constants from the ThermoML Archive, demonstrating that it achieves comparable accuracy to the Generalized Amber Force Field (GAFF) that consists of many thousands of parameters.

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>

Automated Parametrization of Biomolecular Force Fields from Quantum Mechanics/Molecular Mechanics (QM/MM) Simulations through Force Matching

2007 ◽  
Vol 3 (2) ◽  
pp. 628-639 ◽  
Author(s):  
Patrick Maurer ◽  
Alessandro Laio ◽  
Håkan W. Hugosson ◽  
Maria Carola Colombo ◽  
Ursula Rothlisberger
Keyword(s):  
Quantum Mechanics ◽  
Molecular Mechanics ◽  
Force Fields ◽  
Force Matching

scholarly journals "Dividing and Conquering" and "Caching" in Molecular Modeling

2021 ◽  
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Materials Science ◽  
Force Fields ◽  
Coarse Graining ◽  
Divide And Conquer ◽  
Collective Variables ◽  
Molecular Systems ◽  
State Models ◽  
Molecular Science

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

Empirical Classical Force Fields for Molecular Systems

1999 ◽  
pp. 177-214 ◽  
Author(s):  
Philippe H. Hünenberger ◽  
Wilfred F. van Gunsteren
Keyword(s):  
Force Fields ◽  
Molecular Systems

scholarly journals Effects of All-Atom Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of Aβ16–22 Dimer

2019 ◽  
Vol 15 (2) ◽  
pp. 1440-1452 ◽  
Author(s):  
Viet Hoang Man ◽  
Xibing He ◽  
Philippe Derreumaux ◽  
Beihong Ji ◽  
Xiang-Qun Xie ◽  
...  
Keyword(s):  
Molecular Mechanics ◽  
Force Fields ◽  
Amyloid Peptide ◽  
Peptide Assembly

scholarly journals A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes’ reactivity

2019 ◽  
Vol 5 (5) ◽  
pp. eaaw2210 ◽  
Author(s):  
Alessandro Lunghi ◽  
Stefano Sanvito
Keyword(s):  
Organic Molecules ◽  
Energy Surface ◽  
Force Fields ◽  
Covalent Bond ◽  
Organometallic Compounds ◽  
Molecular Systems ◽  
Physical Chemical ◽  
Atomistic Models ◽  
General Potential ◽  
Unified Description

Computational studies of chemical processes taking place over extended size and time scales are inaccessible by electronic structure theories and can be tackled only by atomistic models such as force fields. These have evolved over the years to describe the most diverse systems. However, as we improve the performance of a force field for a particular physical/chemical situation, we are also moving away from a unified description. Here, we demonstrate that a unified picture of the covalent bond is achievable within the framework of machine learning–based force fields. Ridge regression, together with a representation of the atomic environment in terms of bispectrum components, can be used to map a general potential energy surface for molecular systems at chemical accuracy. This protocol sets the ground for the generation of an accurate and universal class of potentials for both organic and organometallic compounds with no specific assumptions on the chemistry involved.

A new electrostatic model for molecular mechanics force fields

2000 ◽  
Vol 556 (1-3) ◽  
pp. 1-21 ◽  
Author(s):  
B. Mannfors ◽  
K. Palmo ◽  
S. Krimm
Keyword(s):  
Molecular Mechanics ◽  
Force Fields ◽  
Electrostatic Model
Sign in / Sign up

Export Citation Format

Share Document