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Pharmacophore modeling and parallel screening for PPAR ligands

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Abstract

We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-α, PPAR-δ, and PPAR-γ. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.

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Acknowledgment

We thank Dr. Rémy D. Hoffmann, Accelrys SARL Paris, for screening the Derwent WDI database.

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Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 52c, 6020, Innsbruck, Austria

    Patrick Markt, Daniela Schuster, Johannes Kirchmair, Christian Laggner & Thierry Langer

  2. Inte:Ligand Softwareentwicklungs- und Consulting GmbH, Clemens Maria Hofbauer-Gasse 6, 2344, Maria Enzersdorf, Austria

    Daniela Schuster, Johannes Kirchmair & Thierry Langer

Authors
  1. Patrick Markt
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  2. Daniela Schuster
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  3. Johannes Kirchmair
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  4. Christian Laggner
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  5. Thierry Langer
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Correspondence to Thierry Langer.

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Markt, P., Schuster, D., Kirchmair, J. et al. Pharmacophore modeling and parallel screening for PPAR ligands. J Comput Aided Mol Des 21, 575–590 (2007). https://doi.org/10.1007/s10822-007-9140-0

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  • DOI: https://doi.org/10.1007/s10822-007-9140-0

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