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From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ
Author(s) -
Rupp Matthias,
Schroeter Timon,
Steri Ramona,
Zettl Heiko,
Proschak Ewgenij,
Hansen Katja,
Rau Oliver,
Schwarz Oliver,
MüllerKuhrt Lutz,
SchubertZsilavecz Manfred,
Müller KlausRobert,
Schneider Gisbert
Publication year - 2010
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.200900469
Subject(s) - computer science , natural product , virtual screening , identification (biology) , transcription factor , product (mathematics) , computational biology , machine learning , artificial intelligence , drug discovery , chemistry , biochemistry , biology , gene , mathematics , botany , geometry
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.