Literature DB >> 26339862

Toward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive.

Kyle A Beauchamp1, Julie M Behr, Ariën S Rustenburg, Christopher I Bayly2, Kenneth Kroenlein3, John D Chodera1.   

Abstract

Atomistic molecular simulations are a powerful way to make quantitative predictions, but the accuracy of these predictions depends entirely on the quality of the force field employed. Although experimental measurements of fundamental physical properties offer a straightforward approach for evaluating force field quality, the bulk of this information has been tied up in formats that are not machine-readable. Compiling benchmark data sets of physical properties from non-machine-readable sources requires substantial human effort and is prone to the accumulation of human errors, hindering the development of reproducible benchmarks of force-field accuracy. Here, we examine the feasibility of benchmarking atomistic force fields against the NIST ThermoML data archive of physicochemical measurements, which aggregates thousands of experimental measurements in a portable, machine-readable, self-annotating IUPAC-standard format. As a proof of concept, we present a detailed benchmark of the generalized Amber small-molecule force field (GAFF) using the AM1-BCC charge model against experimental measurements (specifically, bulk liquid densities and static dielectric constants at ambient pressure) automatically extracted from the archive and discuss the extent of data available for use in larger scale (or continuously performed) benchmarks. The results of even this limited initial benchmark highlight a general problem with fixed-charge force fields in the representation low-dielectric environments, such as those seen in binding cavities or biological membranes.

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Year:  2015        PMID: 26339862      PMCID: PMC4667959          DOI: 10.1021/acs.jpcb.5b06703

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  40 in total

1.  The Protein Data Bank.

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Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation.

Authors:  Araz Jakalian; David B Jack; Christopher I Bayly
Journal:  J Comput Chem       Date:  2002-12       Impact factor: 3.376

3.  How fast-folding proteins fold.

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Journal:  Science       Date:  2011-10-28       Impact factor: 47.728

4.  Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald.

Authors:  Romelia Salomon-Ferrer; Andreas W Götz; Duncan Poole; Scott Le Grand; Ross C Walker
Journal:  J Chem Theory Comput       Date:  2013-08-20       Impact factor: 6.006

5.  Automatic atom type and bond type perception in molecular mechanical calculations.

Authors:  Junmei Wang; Wei Wang; Peter A Kollman; David A Case
Journal:  J Mol Graph Model       Date:  2006-02-03       Impact factor: 2.518

6.  Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the villin headpiece.

Authors:  Daniel L Ensign; Peter M Kasson; Vijay S Pande
Journal:  J Mol Biol       Date:  2007-09-29       Impact factor: 5.469

7.  Conformer generation with OMEGA: learning from the data set and the analysis of failures.

Authors:  Paul C D Hawkins; Anthony Nicholls
Journal:  J Chem Inf Model       Date:  2012-11-12       Impact factor: 4.956

8.  Force Field Benchmark of Organic Liquids. 2. Gibbs Energy of Solvation.

Authors:  Jin Zhang; Badamkhatan Tuguldur; David van der Spoel
Journal:  J Chem Inf Model       Date:  2015-06-03       Impact factor: 4.956

9.  Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles.

Authors:  Robert B Best; Xiao Zhu; Jihyun Shim; Pedro E M Lopes; Jeetain Mittal; Michael Feig; Alexander D Mackerell
Journal:  J Chem Theory Comput       Date:  2012-07-18       Impact factor: 6.006

10.  Improved side-chain torsion potentials for the Amber ff99SB protein force field.

Authors:  Kresten Lindorff-Larsen; Stefano Piana; Kim Palmo; Paul Maragakis; John L Klepeis; Ron O Dror; David E Shaw
Journal:  Proteins       Date:  2010-06
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  5 in total

1.  Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water.

Authors:  Caitlin C Bannan; Gaetano Calabró; Daisy Y Kyu; David L Mobley
Journal:  J Chem Theory Comput       Date:  2016-08-01       Impact factor: 6.006

2.  Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field.

Authors:  Yudong Qiu; Daniel G A Smith; Simon Boothroyd; Hyesu Jang; David F Hahn; Jeffrey Wagner; Caitlin C Bannan; Trevor Gokey; Victoria T Lim; Chaya D Stern; Andrea Rizzi; Bryon Tjanaka; Gary Tresadern; Xavier Lucas; Michael R Shirts; Michael K Gilson; John D Chodera; Christopher I Bayly; David L Mobley; Lee-Ping Wang
Journal:  J Chem Theory Comput       Date:  2021-09-22       Impact factor: 6.578

3.  Escaping Atom Types in Force Fields Using Direct Chemical Perception.

Authors:  David L Mobley; Caitlin C Bannan; Andrea Rizzi; Christopher I Bayly; John D Chodera; Victoria T Lim; Nathan M Lim; Kyle A Beauchamp; David R Slochower; Michael R Shirts; Michael K Gilson; Peter K Eastman
Journal:  J Chem Theory Comput       Date:  2018-10-30       Impact factor: 6.006

4.  A deep learning approach for the blind logP prediction in SAMPL6 challenge.

Authors:  Samarjeet Prasad; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2020-01-30       Impact factor: 3.686

5.  Blinded predictions of distribution coefficients in the SAMPL5 challenge.

Authors:  Stefano Bosisio; Antonia S J S Mey; Julien Michel
Journal:  J Comput Aided Mol Des       Date:  2016-09-27       Impact factor: 3.686

  5 in total

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