Literature DB >> 30351006

Escaping Atom Types in Force Fields Using Direct Chemical Perception.

David L Mobley1,2, Caitlin C Bannan2, Andrea Rizzi3,4, Christopher I Bayly5, John D Chodera4, Victoria T Lim2, Nathan M Lim1, Kyle A Beauchamp6, David R Slochower7, Michael R Shirts8, Michael K Gilson7, Peter K Eastman9.   

Abstract

Traditional approaches to specifying a molecular mechanics force field encode all the information needed to assign force field parameters to a given molecule into a discrete set of atom types. This is equivalent to a representation consisting of a molecular graph comprising a set of vertices, which represent atoms labeled by atom type, and unlabeled edges, which represent chemical bonds. Bond stretch, angle bend, and dihedral parameters are then assigned by looking up bonded pairs, triplets, and quartets of atom types in parameter tables to assign valence terms and using the atom types themselves to assign nonbonded parameters. This approach, which we call indirect chemical perception because it operates on the intermediate graph of atom-typed nodes, creates a number of technical problems. For example, atom types must be sufficiently complex to encode all necessary information about the molecular environment, making it difficult to extend force fields encoded this way. Atom typing also results in a proliferation of redundant parameters applied to chemically equivalent classes of valence terms, needlessly increasing force field complexity. Here, we describe a new approach to assigning force field parameters via direct chemical perception. Rather than working through the intermediary of the atom-typed graph, direct chemical perception operates directly on the unmodified chemical graph of the molecule to assign parameters. In particular, parameters are assigned to each type of force field term (e.g., bond stretch, angle bend, torsion, and Lennard-Jones) based on standard chemical substructure queries implemented via the industry-standard SMARTS chemical perception language, using SMIRKS extensions that permit labeling of specific atoms within a chemical pattern. We use this to implement a new force field format, called the SMIRKS Native Open Force Field (SMIRNOFF) format. We demonstrate the power and generality of this approach using examples of specific molecules that pose problems for indirect chemical perception and construct and validate a minimalist yet very general force field, SMIRNOFF99Frosst. We find that a parameter definition file only ∼300 lines long provides coverage of all but <0.02% of a 5 million molecule drug-like test set. Despite its simplicity, the accuracy of SMIRNOFF99Frosst for small molecule hydration free energies and selected properties of pure organic liquids is similar to that of the General Amber Force Field, whose specification requires thousands of parameters. This force field provides a starting point for further optimization and refitting work to follow.

Entities:  

Year:  2018        PMID: 30351006      PMCID: PMC6245550          DOI: 10.1021/acs.jctc.8b00640

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  67 in total

1.  Predicting hydration free energies using all-atom molecular dynamics simulations and multiple starting conformations.

Authors:  Pavel V Klimovich; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2010-04-06       Impact factor: 3.686

2.  Systematic Parametrization of Polarizable Force Fields from Quantum Chemistry Data.

Authors:  Lee-Ping Wang; Jiahao Chen; Troy Van Voorhis
Journal:  J Chem Theory Comput       Date:  2012-11-29       Impact factor: 6.006

3.  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

4.  Predicting absolute ligand binding free energies to a simple model site.

Authors:  David L Mobley; Alan P Graves; John D Chodera; Andrea C McReynolds; Brian K Shoichet; Ken A Dill
Journal:  J Mol Biol       Date:  2007-06-08       Impact factor: 5.469

5.  Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15.

Authors:  Lee-Ping Wang; Keri A McKiernan; Joseph Gomes; Kyle A Beauchamp; Teresa Head-Gordon; Julia E Rice; William C Swope; Todd J Martínez; Vijay S Pande
Journal:  J Phys Chem B       Date:  2017-04-06       Impact factor: 2.991

6.  Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability-Large-Scale Validation of MD-Based Relative Free Energy Calculations.

Authors:  Thomas Steinbrecher; Chongkai Zhu; Lingle Wang; Robert Abel; Christopher Negron; David Pearlman; Eric Feyfant; Jianxin Duan; Woody Sherman
Journal:  J Mol Biol       Date:  2016-12-10       Impact factor: 5.469

7.  Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset.

Authors:  Michael R Shirts; Christoph Klein; Jason M Swails; Jian Yin; Michael K Gilson; David L Mobley; David A Case; Ellen D Zhong
Journal:  J Comput Aided Mol Des       Date:  2016-10-27       Impact factor: 3.686

8.  Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics.

Authors:  Kai Wang; John D Chodera; Yanzhi Yang; Michael R Shirts
Journal:  J Comput Aided Mol Des       Date:  2013-12-03       Impact factor: 3.686

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

Authors:  Kyle A Beauchamp; Julie M Behr; Ariën S Rustenburg; Christopher I Bayly; Kenneth Kroenlein; John D Chodera
Journal:  J Phys Chem B       Date:  2015-09-29       Impact factor: 2.991

10.  Open Babel: An open chemical toolbox.

Authors:  Noel M O'Boyle; Michael Banck; Craig A James; Chris Morley; Tim Vandermeersch; Geoffrey R Hutchison
Journal:  J Cheminform       Date:  2011-10-07       Impact factor: 5.514

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  27 in total

1.  Data-driven analysis of the number of Lennard-Jones types needed in a force field.

Authors:  Michael Schauperl; Sophie Kantonen; Lee-Ping Wang; Michael K Gilson
Journal:  Commun Chem       Date:  2020-11-13

2.  A fast and high-quality charge model for the next generation general AMBER force field.

Authors:  Xibing He; Viet H Man; Wei Yang; Tai-Sung Lee; Junmei Wang
Journal:  J Chem Phys       Date:  2020-09-21       Impact factor: 3.488

3.  Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol-water log P blind challenge.

Authors:  Shuzhe Wang; Sereina Riniker
Journal:  J Comput Aided Mol Des       Date:  2019-11-19       Impact factor: 3.686

4.  Binding Thermodynamics of Host-Guest Systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative.

Authors:  David R Slochower; Niel M Henriksen; Lee-Ping Wang; John D Chodera; David L Mobley; Michael K Gilson
Journal:  J Chem Theory Comput       Date:  2019-10-25       Impact factor: 6.006

5.  Data-Driven Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters.

Authors:  Sophie M Kantonen; Hari S Muddana; Michael Schauperl; Niel M Henriksen; Lee-Ping Wang; Michael K Gilson
Journal:  J Chem Theory Comput       Date:  2020-01-17       Impact factor: 6.006

6.  Towards Molecular Simulations that are Transparent, Reproducible, Usable By Others, and Extensible (TRUE).

Authors:  Matthew W Thompson; Justin B Gilmer; Ray A Matsumoto; Co D Quach; Parashara Shamaprasad; Alexander H Yang; Christopher R Iacovella; Clare M Cabe; Peter T Cummings
Journal:  Mol Phys       Date:  2020-04-08       Impact factor: 1.962

7.  Multi-phase Boltzmann weighting: accounting for local inhomogeneity in molecular simulations of water-octanol partition coefficients in the SAMPL6 challenge.

Authors:  Andreas Krämer; Phillip S Hudson; Michael R Jones; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2020-02-14       Impact factor: 3.686

8.  Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion.

Authors:  Yalun Yu; Andreas Krämer; Richard M Venable; Andrew C Simmonett; Alexander D MacKerell; Jeffery B Klauda; Richard W Pastor; Bernard R Brooks
Journal:  J Chem Theory Comput       Date:  2021-02-23       Impact factor: 6.006

9.  Improving small molecule force fields by identifying and characterizing small molecules with inconsistent parameters.

Authors:  Jordan N Ehrman; Victoria T Lim; Caitlin C Bannan; Nam Thi; Daisy Y Kyu; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2021-01-28       Impact factor: 3.686

10.  Generalizing the Discrete Gibbs Sampler-Based λ-Dynamics Approach for Multisite Sampling of Many Ligands.

Authors:  Jonah Z Vilseck; Xinqiang Ding; Ryan L Hayes; Charles L Brooks
Journal:  J Chem Theory Comput       Date:  2021-06-08       Impact factor: 6.006

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