Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 183

The DOI matches its Crossref record.

Accurate prediction of basicity in aqueous solution with COSMO‐RS

Eckert, Frank, Klamt, Andreas·2006·Journal of Computational Chemistry

Abstract

The COSMO‐RS method, a combination of the quantum chemical dielectric continuum solvation model COSMO with a statistical thermodynamics treatment for realistic solvation simulations, has been used for the prediction of base pK a constants. For a variety of 43 organic bases the directly calculated values of the free energies of dissociation in water showed a very good correlation with experimental base pK a values (r 2 = 0.98), corresponding to a standard deviation of 0.56 pK a units. Thus, we have an a priori prediction method for base pK a with the regression constant and the slope as only adjusted parameters. In accord with recent findings for pK a acidity predictions, the slope of pK a vs. Δ Gdiss was significantly smaller than the theoretically expected value of 1/RT ln(10). The predictivity of the presented method is general and not restricted to certain compound classes, but systematic corrections of 1 and 2 pKa units for secondary and tertiary aliphatic amines are required, respectively. The pK a prediction method was validated on a set of 58 complex multifunctional drug‐like compounds, yielding an RMS accuracy of 0.66 pK a units. © 2005 Wiley Periodicals, Inc. J Comput Chem 27: 11–19, 2006

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/jcc.20309

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 183

Metadata

Authors
Eckert, F., & Klamt, A.
Venue
Journal of Computational Chemistry
Publisher
Wiley
Published
2005-10-18
Volume
27
Issue
1
Pages
11-19
ISSN
  • 0192-8651 (ISSN-L)
  • 1096-987X
OpenAlex
W2126881685
Cited by
183
References
0
FWCI
4.51
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Accurate prediction of basicity in aqueous solution with COSMO‐RS. https://app.cochat.ai/papers/10.1002/jcc.20309
The paper
Eckert, F., & Klamt, A. (2006). Accurate prediction of basicity in aqueous solution with COSMO‐RS. Journal of Computational Chemistry. https://doi.org/10.1002/jcc.20309

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.