Volume 6 Supplement 1

9th German Conference on Chemoinformatics

Open Access

Empirical charges for chemoinformatics applications

  • Tomáš Bouchal1Email author,
  • Radka Svobodová Vařeková1,
  • Tomáš Raček2,
  • Crina-Maria Ionescu1,
  • Stanislav Geidl1,
  • Aleš Křenek2 and
  • Jaroslav Koča1
Journal of Cheminformatics20146(Suppl 1):P60

DOI: 10.1186/1758-2946-6-S1-P60

Published: 11 March 2014

Partial atomic charges describe the distribution of electron density in a molecule, and therefore they provide clues regarding the chemical behaviour of molecules. Atomic charges are frequently used in molecular modelling applications such as molecular dynamics, docking, conformational searches, binding site prediction, etc. Recently, partial atomic charges have also become popular chemoinformatics descriptors [1].

Partial atomic charges cannot be determined experimentally, and they are also not quantum mechanical observables. For this reason, many different methods have been developed for their calculation. These charge calculation methods can be divided into two main groups, namely quantum mechanical (QM) approaches and empirical approaches. QM approaches provide accurate charges, but they are very slow and therefore not feasible for large sets of molecules. Empirical charges can be calculated quickly and their accuracy is similar to QM, making empirical charges more appropriate for chemoinformatics applications. A very useful empirical charge calculation method is EEM (Electronegativity Equalization Method) [2, 3]. This method provides charges comparable to the QM approach for which the given EEM model was parameterized. The weak point of this empirical method, as well as of other empirical methods, is the necessity for parameterization, and also the insufficient coverage of currently available EEM model parameters.

In our work, we first analysed, how applicable are currently published EEM parameters in chemoinformatics. Specifically, how many molecules from databases of known organic compounds (Pubchem, ZINC, Drugbank etc.) they can cover. We found, the coverage is about 50-75%. We would like to show a methodology for preparation of parameters with higher coverage (>95% of molecules) and also its results.

Authors’ Affiliations

(1)
National Centre for Biomolecular Research, Faculty of Science and CEITEC - Central European Institute of Technology, Masaryk University
(2)
Institute of Computer Science and Faculty of Informatics, Masaryk University

References

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  3. Svobodova Varekova R, Jirouskova Z, Vanek J, Suchomel S, Koca J: Electronegativity equalization method: Parameterization and validation for large sets of organic, organohalogene and organometal molecule. Int J Mol Sci. 2007, 8: 572-582. 10.3390/i8070572.View ArticleGoogle Scholar

Copyright

© Bouchal et al; licensee Chemistry Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.