Evidence map›Paper›PMID 27602298›Full record

ArticlePeerJ2016

Prediction of pKa values using the PM6 semiempirical method.

Jimmy C Kromann, Frej Larsen, Hadeel Moustafa, Jan H Jensen

Abstract read
In one paragraph

Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. On the Accuracy of the Direct Method to Calculate pThe journal of physical chemistry. A · 2021
    Article
  4. Article
  5. Absolute and relative pKJournal of computer-aided molecular design · 2018
    Article
  6. Article
  7. Correlations between theJournal of molecular modeling · 2018
    Article
  8. Article
  9. 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

4 authors.

Jimmy C KromannDepartment of Chemistry, University of Copenhagen , Copenhagen , Denmark.
Frej LarsenDepartment of Chemistry, University of Copenhagen , Copenhagen , Denmark.
Hadeel MoustafaDepartment of Chemistry, University of Copenhagen , Copenhagen , Denmark.
Jan H JensenDepartment of Chemistry, University of Copenhagen , Copenhagen , Denmark.ORCID 0000-0002-1465-1010

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs) of 0.6-0.7 pH units. For carboxylic acids, the accuracy (0.7-1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5-0.6), slightly worse (0.5-1.0), and worse (1.0-2.5), provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2-5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

Indexed as

Drug designElectronic structurepKa predictionSemiempirical methods

Identifiers

PMID27602298
PMCPMC4991863

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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.