Volume 6 Supplement 1

9th German Conference on Chemoinformatics

Open Access

Target prediction by cascaded self-organizing maps for ligand de-orphaning and side-effect investigation

  • Daniel Reker1,
  • Tiago Rodrigues1,
  • Petra Schneider1 and
  • Gisbert Schneider1
Journal of Cheminformatics20146(Suppl 1):P47

DOI: 10.1186/1758-2946-6-S1-P47

Published: 11 March 2014

Computational chemogenomics approaches have emerged as a means to predict modulations of biomolecules by ligands. We implemented a method for the prediction of the macromolecular targets of small molecules combining state-of-the-art approaches that compare physicochemical properties and pharmacophoric features of query molecules with known drugs. Investigating similarity from multiple vantage points has been shown to increase the prediction accuracy in a retrospective evaluation. The method has been applied in multiple projects to “de-orphan” molecules with unknown main target and investigate potential side-effects of drug candidates. In a first application, the method identified a molecular scaffold as a potentially privileged structure of druglike compounds for chemoresistant tumor therapy [1]. In a second project, the tool revealed the potential of up to 5% of known bioactive substances to have unrecognized epigenetic effects by modulating histone deacetylase (HDAC) activity – thereby stressing the importance of probing for epigenetic effects in long-term drug toxicity studies [2].

Authors’ Affiliations

(1)
Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH)

References

  1. Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, Schneider G: Mol Inf. 2013, 32: 133-138. 10.1002/minf.201200141.View ArticleGoogle Scholar
  2. Lötsch J, Schneider G, Reker D, Parnham MJ, Schneider P, Geisslinger G, Doehring A: Trends Mol Med. 2013,Google Scholar

Copyright

© Reker 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.