Evidence map›Paper›PMID 22737045›Full record

ArticleJournal of chemical theory and computation2012

Calculation of Host-Guest Binding Affinities Using a Quantum-Mechanical Energy Model.

Hari S Muddana, Michael K Gilson

Abstract read
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Article in Journal of chemical theory and computation, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Recent Developments and Applications of the MMPBSA Method.Frontiers in molecular biosciences · 2017
    Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Enhanced semiempirical QM methods for biomolecular interactions.Computational and structural biotechnology journal · 2015
    Review
  12. Article
  13. Article
  14. Variational Implicit Solvation with Poisson-Boltzmann Theory.Journal of chemical theory and computation · 2014
    Article
  15. The SAMPL4 host-guest blind prediction challenge: an overview.Journal of computer-aided molecular design · 2014
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

2 authors.

Hari S MuddanaSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093-0736.
Michael K Gilson

Funding

THEORY AND MODELING OF NONCOVALENT BINDINGR01GM061300 · NIGMS · UNIVERSITY OF MD BIOTECHNOLOGY INSTITUTE · PI GILSON, MICHAEL K. · 2000 to 2021
$7.2M
NIGMS NIH HHS R01 GM061300
6 · The paper itself

Abstract

The prediction of protein-ligand binding affinities is of central interest in computer-aided drug discovery, but it is still difficult to achieve a high degree of accuracy. Recent studies suggesting that available force fields may be a key source of error motivate the present study, which reports the first mining minima (M2) binding affinity calculations based on a quantum mechanical energy model, rather than an empirical force field. We apply a semi-empirical quantum-mechanical energy function, PM6-DH+, coupled with the COSMO solvation model, to 29 host-guest systems with a wide range of measured binding affinities. After correction for a systematic error, which appears to derive from the treatment of polar solvation, the computed absolute binding affinities agree well with experimental measurements, with a mean error 1.6 kcal/mol and a correlation coefficient of 0.91. These calculations also delineate the contributions of various energy components, including solute energy, configurational entropy, and solvation free energy, to the binding free energies of these host-guest complexes. Comparison with our previous calculations, which used empirical force fields, point to significant differences in both the energetic and entropic components of the binding free energy. The present study demonstrates successful combination of a quantum mechanical Hamiltonian with the M2 affinity method.

Identifiers

PMID22737045
PMCPMC3378313

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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.