Machine Learning to Develop Peptide Catalysts─Successes, Limitations, and Opportunities

Tobias Schnitzer, Martin Schnurr, Andrew F. Zahrt, Nader Sakhaee, Scott E. Denmark, and Helma Wennemers
ACS Central Science 2024 10 (2), 367-373

DOI: 10.1021/acscentsci.3c01284


Peptides have been established as modular catalysts for various transformations. Still, the vast number of potential amino acid building blocks renders the identification of peptides with desired catalytic activity challenging. Here, we develop a machine-learning workflow for the optimization of peptide catalysts. First─in a hypothetical competition─we challenged our workflow to identify peptide catalysts for the conjugate addition reaction of aldehydes to nitroolefins and compared the performance of the predicted structures with those optimized in our laboratory. On the basis of the positive results, we established a universal training set (UTS) containing 161 catalysts to sample an in silico library of ∼30,000 tripeptide members. Finally, we challenged our machine learning strategy to identify a member of the library as a stereoselective catalyst for an annulation reaction that has not been catalyzed by a peptide thus far. We conclude with a comparison of data-driven versus expert-knowledge-guided peptide catalyst optimization.


Synthetic protocols and analytical data of peptides and catalysis products, and details on the computational methods.