环状多肽在药物研发方面的进展

Due to their unique advantages, such as stability, membrane permeability, and high affinity, cyclic peptides (CPs) have become an important part of the peptide drug market and demonstrate significant potential in the field of new drug development. To meet the demands of high-throughput screening in new drug development and address the diverse cyclization mechanisms and modification strategies, as well as the incorporation of non-natural amino acids in cyclic peptide drugs, a series of novel synthetic methods have been developed in recent years. Meanwhile, more and more computational methods applicable to CP systems have been established, greatly improving the efficiency of developing CP drugs and expanding the chemical space. This paper provides a systematic review of: (1) the latest research progress on representative CP synthesis methods; (2) emerging computational design methods, including methods for predicting and generating stable structures of CPs with diverse structural units and cyclization mechanisms, methods for predicting CP conformational ensembles, as well as the establishment of CP datasets and prediction tools related to the drug-like properties of CPs; (3) typical cases of CP drug development that combine computational and experimental tools, such as the design of monostable conformation CPs and transmembrane CPs. Finally, this review discusses the potential applications of machine learning technology in CP drug discovery.