C Matijssen

F Milletti

GK Kinsella

H van de Waterbeemd

Journal of Cheminformatics

English

PubMed

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A comparative study of in silico prediction of pKa

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POSTER PRESENTATION Open Access
A comparative study of in silico prediction of pKa
C Matijssen
From 5th German Conference on Cheminformatics: 23. CIC-Workshop
Goslar, Germany. 8-10 November 2009
The ionization constant (pKa) is the measure of the
strength of an acid or base in a solution. Most ligands
act as a weak acid or base. Accurate determination of
pKa is important as it can improve the pharmaceutical
properties of a compound [1]. In general, charged com-
pounds have better solubility, but are less effective in
membrane permeation. Therefore, optimization of the
pharmacokinetic profile of a compound can be per-
formed by increasing or decreasing its ionization by
changing functional groups. Another role for optimiza-
tion of the charged group could be the interaction with
its target. Changing the charge on the functional group
could improve the interaction with its associated target
and result in improved binding affinity.
Different methods have been developed for the com-
putational determination of pKa values. These can be
based on different methods such as QSAR [2] or quan-
tum chemistry approaches [3]. These two methods differ
considerable in terms of computational resource and
hence, the time needed for a prediction. Depending on
the number of ligands that needed prediction and time
available, a choice for one method can be made.
A comparison between several pKa predictors (Pipe-
line Pilot, Moka, Epik and Jaguar) was made. All meth-
ods perform well when a diverse set of ligands which
covers a range of pKa values is considered. However,
when optimizing a series of compounds the influence of
small changes to the molecule and its effect on pKa
becomes more difficult to predict.
Published: 4 May 2010
References
1. Waterbeemd van de H, Gifford E: Nature Rev Drug Disc 2003, 2:192.
2. Milletti F, Storchi L, Sforna G, Cruciani G: J Chem Inf Model 2007, 47:2172.
3. Kinsella GK, Rodriguez F, Watson GW, Rozas I: Bioorg Med Chem
2007, 15:2850.
doi:10.1186/1758-2946-2-S1-P37
Cite this article as: Matijssen: A comparative study of in silico prediction
of pKa. Journal of Cheminformatics 2010 2(Suppl 1):P37.
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Matijssen Journal of Cheminformatics 2010, 2(Suppl 1):P37
http://www.jcheminf.com/content/2/S1/P37
© 2010 Matijssen; licensee BioMed Central Ltd.


Bioorg Med Chem 2007, 15:2850. doi:10.1186/1758-2946-2-S1-P37 Cite this article as: Matijssen: A comparative study of in silico prediction of pKa.

E: Nature Rev Drug Disc

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A comparative study of in silico prediction of pKa

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