Volume 3 Supplement 1

6th German Conference on Chemoinformatics, GCC 2010

Open Access

Rational, computer-aided design of multi-target ligands

Journal of Cheminformatics20113(Suppl 1):P10

DOI: 10.1186/1758-2946-3-S1-P10

Published: 19 April 2011

Over the past two decades the “one drug – one target – one disease” concept became the prevalent paradigm in drug discovery. The main idea of this approach is the identification of a single protein target whose inhibition leads to a successful treatment of the examined disease. The predominant assumption is that highly selective ligands would avoid unwanted side effects caused by binding to secondary non-therapeutic targets.

In recent years the results of post-genomic and network biology showed that proteins rarely act in isolated systems but rather as a part of a highly connected network [1]. In addition this connectivity leads to more robust systems that cannot be interfered by the inhibition of a single target of that network and consequently might not lead to the desired therapeutic effect [2]. Furthermore studies prove that robust systems are rather affected by weak inhibitions of several parts than by a complete inhibition of a single selected element of that system [3].

Therefore there is an increasing interest in developing drugs that take effect on multiple targets simultaneously but is concurrently a great challenge for medicinal chemists. There has to be a sufficient activity on each target as well as an adequate pharmacokinetic profile [4]. Early design strategies tried to link the pharmacophors of known inhibitors, however these methods often lead to high molecular weight and low ligand efficacy.

We present a new rational approach based on a retrosynthetic combinatorial analysis procedure [5] on approved ligands of multiple targets. These RECAP fragments are used to design a large combinatorial library containing molecules featuring chemical properties of each ligand class. The molecules are further validated by machine learning models, like random forests and self-organizing maps, regarding their activity on the targets of interest.

Authors’ Affiliations

(1)
Pharmaceutical Chemistry, Goethe University

References

  1. Jeong H, Mason SP, Barabási AL, Oltvai ZN: Lethality and centrality in protein networks. Nature. 2001, 411: 41-42. 10.1038/35075138.View ArticleGoogle Scholar
  2. Kitano H: Towards a theory of biological robustness. Molecular Systems Biology. 2007, 3: 137-10.1038/msb4100179.View ArticleGoogle Scholar
  3. Agoston V, Csermely P, Pongor S: Multiple weak hits confuse complex systems: a transcriptional regulatory network as an example. Phys Rev E. 2005, 71: 051909-10.1103/PhysRevE.71.051909.View ArticleGoogle Scholar
  4. Morphy R, Rankovic Z: Designing multiple ligands - medicinal chemistry strategies and challenges. Curr Pharm Design. 2009, 15: 587-600. 10.2174/138161209787315594.View ArticleGoogle Scholar
  5. Lewell XQ, Judd DB, Watson SP, Hann MM: RECAP - retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci. 1998, 18: 511-522.View ArticleGoogle Scholar

Copyright

© Achenbach and Proschak; licensee BioMed Central Ltd. 2011

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.