Evidence map›Paper›PMID 26339862›Full record

ArticleThe journal of physical chemistry. B2015

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

Kyle A Beauchamp, Julie M Behr, Ariën S Rustenburg, Christopher I Bayly, Kenneth Kroenlein, John D Chodera

Abstract read
In one paragraph

Article in The journal of physical chemistry. B, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. A Fast, Convenient, Polarizable Electrostatic Model for Molecular Dynamics.Journal of chemical theory and computation · 2024
    Article
  3. Article
  4. Article
  5. Keyphrase Extraction for Technical Language Processing.Journal of research of the National Institute of Standards and Technology · 2021
    Article
  6. A deep learning approach for the blind logP prediction in SAMPL6 challenge.Journal of computer-aided molecular design · 2020
    Article
  7. Article
  8. Escaping Atom Types in Force Fields Using Direct Chemical Perception.Journal of chemical theory and computation · 2018
    Article
  9. Blinded predictions of distribution coefficients in the SAMPL5 challenge.Journal of computer-aided molecular design · 2016
    Article
  10. Article
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

6 authors.

Kyle A BeauchampComputational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center , New York, New York 10065, United States.
Julie M Behr
Ariën S Rustenburg
Christopher I BaylyOpenEye Scientific Software Inc. , Santa Fe, New Mexico 87508, United States.
Kenneth KroenleinThermodynamics Research Center, NIST , Boulder, Colorado 80305, United States.
John D ChoderaComputational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center , New York, New York 10065, United States.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
Tri-Institutional Training Program in Computational Biology & MedicineT32GM083937 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI CHRISTINI, DAVID J. · 2009 to 2018
$3.1M
NCI NIH HHS P30 CA008748NIGMS NIH HHS 1T32GM083937NIGMS NIH HHS T32 GM083937
6 · The paper itself

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.

Indexed as

AutomationElectricityInformation Storage and Retrieval

Identifiers

PMID26339862
PMCPMC4667959

What OpenQuestion holds

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Registered trials

None linked

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.