Our group aims to develop small molecule catalysts with target-selectivity. Specifically, we explore the activity of extensive peptide libraries with machine learning to predict catalysts that only engage with a specific substrate, a particular reaction site, or a group within a certain environment.

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Target-selectivity, the activity for a specific substrate, reaction site, or sequence, is a hallmark of nature. Equipping small molecule catalysts with nature`s level of precision will boost efficacy and sustainability of chemical synthesis.

 

Data-driven workflows and machine learning have opened up new perspectives for catalyst development. Instead of „trial-and-error“ and rational design approaches, analysis of large amounts of data enables targeted identification of catalysts. Remarkably, data-driven workflows to identify catalysts with substrate-, site-, or sequence-selectivity remain largely unexplored.

 

Peptides have emerged as powerful organocatalysts. They are composed of amino acids, which are linked by repetitive amide couplings. Due to their modular structure, peptide synthesis is typically fast, can be automated, and performed on large scale (g to multi-kg amounts). Hence, big catalyst libraries with an enormous functional and structural breadth are easily obtained.