Evidence map›Paper›PMID 40033678›Full record

ArticleJournal of chemical theory and computation2025

Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.

Anastasia Croitoru, Anmol Kumar, Jean-Christophe Lambry, Jihyeon Lee, Suliman Sharif, Wenbo Yu, Alexander D MacKerell, Alexey Aleksandrov

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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  12. The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025
    Review
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

8 authors.

Anastasia CroitoruLaboratoire d'Optique et Biosciences (CNRS UMR7645, INSERM U1182), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau F-91128, France.ORCID 0000-0003-1335-3940
Anmol KumarDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States.ORCID 0000-0002-4691-2086
Jean-Christophe LambryLaboratoire d'Optique et Biosciences (CNRS UMR7645, INSERM U1182), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau F-91128, France.ORCID 0000-0001-8264-0391
Jihyeon LeeDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States.
Suliman SharifDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States.
Wenbo YuDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States.ORCID 0000-0001-6962-5314
Alexander D MacKerellDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States.ORCID 0000-0001-8287-6804
Alexey AleksandrovLaboratoire d'Optique et Biosciences (CNRS UMR7645, INSERM U1182), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau F-91128, France.ORCID 0000-0002-8150-3931

Funding

Macromolecular Conformational HeterogeneityR35GM131710 · NIGMS · UNIVERSITY OF MARYLAND BALTIMORE · PI ALEXANDER D MACKERELL · 2019 to 2026
$6.2M
NIGMS NIH HHS R35 GM131710
6 · The paper itself

Abstract

Small molecule empirical force fields (FFs), including the CHARMM General Force Field (CGenFF), are designed to have wide coverage of organic molecules and to rapidly assign parameters to molecules not explicitly included in the FF. Assignment of parameters to new molecules in CGenFF is based on a trained bond-angle-dihedral charge increment linear interpolation scheme for the partial atomic charges along with bonded parameters assigned based on analogy using a rules-based penalty score scheme associated with atom types and chemical connectivity. Accordingly, the accuracy of CGenFF is related to the extent of the training set of available parameters. In the present study that training set is extended by 1390 molecules selected to represent connectivities new to CGenFF training compounds. Quantum mechanical (QM) data for optimized geometries, bond, valence angle, and dihedral angle potential energy scans, interactions with water, molecular dipole moments, and electrostatic potentials were used as target data. The resultant bonded parameters and partial atomic charges were used to train a new version of the CGenFF program, v5.0, which was used to generate parameters for a validation set of molecules, including drug-like molecules approved by the FDA, which were then benchmarked against both experimental and QM data. CGenFF v5.0 shows overall improvements with respect to QM intramolecular geometries, vibrations, dihedral potential energy scans, dipole moments and interactions with water. Tests of pure solvent properties of 216 molecules show small improvements versus the previous release of CGenFF v2.5.1 reflecting the high quality of the Lennard-Jones parameters that were explicitly optimized during the initial optimization of both the CGenFF and the CHARMM36 force field. CGenFF v5.0 represents an improvement that is anticipated to more accurately model intramolecular geometries and strain energies as well as noncovalent interactions of drug-like and other organic molecules.

Identifiers

PMID40033678
PMCPMC11938330

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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.