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

doi:10.1002/cmdc.200900469

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 journal › Article › peer-review

50 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 language	English
Pages (from-to)	191-194
Number of pages	4
Journal	ChemMedChem
Volume	5
Issue number	2
DOIs	https://doi.org/10.1002/cmdc.200900469
Publication status	Published - 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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10.1002/cmdc.200900469

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AU - Rupp, Matthias

AU - Schroeter, Timon

AU - Steri, Ramona

AU - Zettl, Heiko

AU - Proschak, Ewgenij

AU - Hansen, Katja

AU - Rau, Oliver

AU - Schwarz, Oliver

AU - Müller-Kuhrt, Lutz

AU - Schubert-Zsilavecz, Manfred

AU - Müller, Klaus Robert

AU - Schneider, Gisbert

PY - 2010/2/1

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AB - 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)

KW - Drug design

KW - Machine learning

KW - NMR

KW - Natural products

KW - Virtual screening

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

Abstract

Keywords

ASJC Scopus subject areas

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