Evidence map›Paper›PMID 30351006›Full record

ArticleJournal of chemical theory and computation2018

Escaping Atom Types in Force Fields Using Direct Chemical Perception.

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 and 2 more

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

95 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Reparameterization of the Amber RNA Force Field Non-Bonded Terms.bioRxiv : the preprint server for biology · 2026
    Article
  7. Article
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  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

35 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

David L MobleyDepartment of Pharmaceutical Science , University of California , Irvine , California 92697 , United States.ORCID http://orcid.org/0000-0002-1083-5533
Caitlin C BannanDepartment of Chemistry , University of California , Irvine , California 92697 , United States.ORCID http://orcid.org/0000-0003-2777-1174
Andrea RizziTri-Institutional Program in Computational Biology and Medicine , New York , New York 10065 , United States.
Christopher I BaylyOpenEye Scientific Software , Santa Fe , New Mexico 87507 , United States.
John D ChoderaComputational and Systems Biology Program , Memorial Sloan Kettering Cancer Center , New York , New York 10065 , United States.ORCID http://orcid.org/0000-0003-0542-119X
Victoria T LimDepartment of Chemistry , University of California , Irvine , California 92697 , United States.
Nathan M LimDepartment of Pharmaceutical Science , University of California , Irvine , California 92697 , United States.
Kyle A BeauchampCounsyl , South San Francisco , California 94080 , United States.
David R SlochowerSkaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States.
Michael R ShirtsDepartment of Chemical and Biological Engineering , University of Colorado Boulder , Boulder , Colorado 80309 , United States.ORCID http://orcid.org/0000-0003-3249-1097
Michael K GilsonSkaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States.
Peter K EastmanDepartment of Chemistry , Stanford University , Stanford , California 94305 , United States.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
THEORY AND MODELING OF NONCOVALENT BINDINGR01GM061300 · NIGMS · UNIVERSITY OF MD BIOTECHNOLOGY INSTITUTE · PI GILSON, MICHAEL K. · 2000 to 2021
$7.2M
Computational alchemy for molecular design and optimizationR01GM108889 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI MOBLEY, DAVID LOWELL · 2014 to 2022
$2.5M
Advancing predictive physical modeling through focused development of model systems to drive new modeling innovationsR01GM124270 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI MOBLEY, DAVID LOWELL · 2018 to 2021
$1.5M
NCI NIH HHS P30 CA008748NIGMS NIH HHS R01 GM061300NIGMS NIH HHS R01 GM108889NIGMS NIH HHS R01 GM124270
6 · The paper itself

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.

Identifiers

PMID30351006
PMCPMC6245550

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.