Dataset of Chiral Ligands derived from Amino Acids and Peptides for Metal catalysis

Published: 10 May 2024| Version 1 | DOI: 10.17632/ptpfztszrn.1
Benjamin Gung,


This dataset provides a list of amino acids and peptides reported in the literature proving to be effective ligands for metal centered catalysts. Several parameters were evaluated, including amino acid combination, metal atom, carboxyl and amino protecting groups, modification of natural amino acid, and mechanism of catalysis. Along with the analysis of physical-chemical properties, the SMILES representation for each amino acid and/or peptide was generated to provide an easy-to use means of training machine learning models. This offers an opportunity for the development of improved peptide ligands for enantioselective metal-centered catalysts. Being a reliable manually curated dataset, it enables the benchmark for comparison of new termini functional groups. Moreover, the dataset provides an insight in the structures of the more successful peptide ligands and can be used as the foundation for the development of next generation of peptide-based chiral ligands.


Steps to reproduce

Data collection was based on literature reports. A literature search was conducted to identify all the reported amino acids and peptide sequences with capability of serving as metal ligands to form enantioselective catalysts.


Miami University


Organic Chemistry, Organic Synthesis, Enantioselective Catalysis


Miami University