Amino acids and peptides are readily available biomolecules and can function as chiral ligands for transition metal catalysis. An example is the copper complex catalyzed 1,4-addition of dialkylzinc to acyclic enones, which employs peptide ligands. This review provides a dataset of amino acids and peptides reported in the literature proving to be effective ligands for metal-centered catalysts. Several parameters were highlighted, including amino acid combination, metal atoms, carboxyl and amino protecting groups, modification of natural amino acids, and the mechanism of catalysis. Along with analyzing physical-chemical properties, the SMILES representation for each amino acid and/or peptide was generated and made available online, providing an easy-to-use means of training machine learning models. This review offers an opportunity for the development of more efficient peptide ligands for enantioselective metal-centered catalysts. The available online dataset is a reliable manually curated table, it enables the benchmark for comparison of new terminal functional groups. Moreover, the review provides insight into the structures of the more successful peptide ligands and can be used as the foundation for the development of the next generation of peptide-based chiral ligands.
Read full abstract