From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ

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. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.