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

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.