From machine learning to natural product derivatives that selectively activate transcription factor PPARγ

Matthias Rupp, Timon Schroeter, Ramona Steri, Heiko Zettl, Ewgenij Proschak, Katja Hansen, Oliver Rau, Oliver Schwarz, Lutz Müller-Kuhrt, Manfred Schubert-Zsilavecz, Klaus Robert Müller, Gisbert Schneider

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

Advanced kernel-based machine learning methods enable the identification of innovative bioactive compounds with minimal experimental effort. Comparative virtual screening revealed that nonlinear models of the underlying structure-activity relationship are necessary for successful compound picking. In a proof-of-concept study a novel truxillic acid derivative was found to selectively activate transcription factor PPARγ. (Chemical Equation Presented)

Original languageEnglish
Pages (from-to)191-194
Number of pages4
JournalChemMedChem
Volume5
Issue number2
DOIs
Publication statusPublished - 2010 Feb 1

Keywords

  • Drug design
  • Machine learning
  • NMR
  • Natural products
  • Virtual screening

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Pharmacology
  • Drug Discovery
  • Pharmacology, Toxicology and Pharmaceutics(all)
  • Organic Chemistry

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  • Cite this

    Rupp, M., Schroeter, T., Steri, R., Zettl, H., Proschak, E., Hansen, K., Rau, O., Schwarz, O., Müller-Kuhrt, L., Schubert-Zsilavecz, M., Müller, K. R., & Schneider, G. (2010). From machine learning to natural product derivatives that selectively activate transcription factor PPARγ. ChemMedChem, 5(2), 191-194. https://doi.org/10.1002/cmdc.200900469