Evidence map›Paper›PMID 38717640›Full record

ArticleJournal of chemical information and modeling2024

Exploring the Limits of the Generalized CHARMM and AMBER Force Fields through Predictions of Hydration Free Energy of Small Molecules.

Arghya Chakravorty, Azam Hussain, Luis F Cervantes, Thanh T Lai, Charles L Brooks

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 2024. 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. Article
  3. Article
  4. Active components ofFrontiers in cellular and infection microbiology · 2026
    Article
  5. ADMET & DMPK · 2026
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

5 authors.

Arghya ChakravortyDepartment of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.ORCID 0000-0002-4467-1135
Azam HussainDepartment of Macromolecular Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States.
Luis F CervantesDepartment of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.
Thanh T LaiBiophysics Program, Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.
Charles L BrooksDepartment of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.ORCID 0000-0002-8149-5417

Funding

Theory and Modeling of Biomolecules and their Interactions - Equipment SupplementR35GM130587 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHARLES L BROOKS · 2019 to 2026
$6.9M
NIGMS NIH HHS R35 GM130587
6 · The paper itself

Abstract

Accurate force field parameters, potential energy functions, and receptor-ligand models are essential for modeling the solvation and binding of drug-like molecules to a receptor. A large and ever-growing chemical space of medicinally relevant scaffolds has also required these factors, especially force field parameters, to be highly transferable. Generalized force fields such as the CHARMM General Force Field (CGenFF) and the generalized AMBER force field (GAFF) have accomplished this feat along with other contemporaneous ones like OPLS. Here, we analyze the limits in the parametrization of drug-like small molecules by CGenFF and GAFF in terms of the various functional groups represented within them. Specifically, we link the presence of specific functional groups to the error in the absolute hydration free energy of over 600 small molecules, predicted by alchemical free energy methods implemented in the CHARMM program. Our investigation reveals that molecules with (i) a nitro group in CGenFF and GAFF are, respectively, over- or undersolubilized in aqueous medium, (ii) amine groups are undersolubilized more so in CGenFF than in GAFF, and (iii) carboxyl groups are more oversolubilized in GAFF than in CGenFF. We present our analyses of the potential factors underlying these trends. We also showcase the use of a machine-learning-based approach combined with the SHapley Additive exPlanations framework to attribute these trends to specific functional groups, which can be easily adopted to explore the limits of other general force fields.

Indexed as

ThermodynamicsWaterLigandsMolecular Dynamics SimulationSmall Molecule LibrariesLigandsSmall Molecule LibrariesWater

Identifiers

PMID38717640
PMCPMC11275216

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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.