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

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.本数据集收录了文献报道的、可作为金属中心催化剂有效配体（ligand）的氨基酸与多肽列表。本数据集评估了多项参数，涵盖氨基酸组合、金属原子、羧基与氨基保护基、天然氨基酸修饰方式及催化机制。结合理化性质分析，本数据集为每条氨基酸或多肽生成了SMILES表征，为机器学习模型的训练提供了便捷易用的途径。这为开发用于对映选择性金属中心催化剂的优化型多肽配体提供了契机。作为经人工整理的可靠数据集，本数据集可作为对比新型末端官能团的基准测试集。此外，本数据集可揭示性能更优异的多肽配体的结构特征，亦可作为开发下一代基于多肽的手性配体的基础。