PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

From machine learning to natural product derivatives selectively activating transcription factor PPAR-γ
Matthias Rupp, Timon Schröter, Ramona Steri, Heiko Zettl, Ewgenij Proschak, Katja Hansen, Oliver Rau, Oliver Schwarz, Lutz Müller-Kuhrt, Manfred Schubert-Zsilavecz, Klaus-Robert Müller and Gisbert Schneider
ChemMedChem Volume 5, Number 2, pp. 191-194, 2010. ISSN 1860-7187

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 PPAR-γ.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6917
Deposited By:Katja Hansen
Deposited On:15 March 2010