macrocyclic peptide libraries

Macrocyclic scaffolds are increasingly valued in modern drug discovery for their exceptional activity against undruggable targets (proteases, kinases, PPIs). 2026 marks a key commercial breakthrough for oral macrocyclic peptides: enlicitide, the world’s first oral PCSK9 macrocyclic peptide, has received FDA approval. Macrocyclic candidates targeting KRAS and other classic undruggable targets have also entered clinical development, validating macrocyclization as an effective strategy to overcome druggability barriers.

Two core R&D directions lead current macrocyclic drug design: AI-driven de novo generation and structural optimization of small-molecule macrocycles, and macrocyclic peptides based on sequence design and conformational engineering. Macrocycle druggability hinges on embedded linkers, which determine cyclization efficiency, final conformation and drug-like properties. Bifunctional reaction orthogonality is the core linker selection criterion. Our linker library enables stepwise intramolecular cyclization with suppressed side reactions, accommodates varied ring sizes, and covers three key reaction systems: amide condensation, nucleophilic substitution and CuAAC click chemistry.

Built on classical macrocyclization systems, the library is processed through reaction classification, bifunctional orthogonality evaluation, novelty clustering and redundancy removal, with PROTAC long-chain and ADC cleavable linkers explicitly excluded. Featuring rigid, semi-rigid and flexible scaffolds, it is widely applicable to small-molecule macrocycle synthesis and linear peptide cyclization.

Macrocyclic compounds (≥12-atom cyclic small molecules/peptides) have unique physicochemical properties. They form preorganized conformations with high binding affinity/selectivity, target traditional small-molecule-inaccessible proteins, and bridge small-molecule drugs and biological agents. As key protein phosphorylation enzymes, kinases are linked to tumors, COPD, etc., and are critical therapeutic targets. Traditional small-molecule kinase inhibitors lack selectivity, causing off-target toxicity, low bioavailability, and acquired resistance. Macrocycles’ semi-rigid structure restricts conformations, boosts binding selectivity, optimizes pharmacokinetics, and makes macrocyclization a core kinase inhibitor optimization strategy.

Thousands of bioactive macrocycles were curated from ChEMBL. Via Transformer, macrocyclization was converted into a chemical language translation task, enabling end-to-end macrocycle generation from linear precursors with simplified inputs. Macformer achieves efficient, automated linear molecule macrocyclization via deep learning; generated macrocycles have diversity, novelty, biocompatibility, and cover broader chemical space.

MCE collected thousands of marketed/clinical kinase inhibitors, using their fragments for macrocyclization to generate derivatives. After evaluating synthetic accessibility and physicochemical properties, a million-scale virtual macrocyclic library was built for kinase-related virtual and AI-driven screening.

Macrocyclic compounds (≥12-atom cyclic small molecules/peptides) have unique physicochemical properties. They form preorganized conformations with high binding affinity/selectivity, target traditional small-molecule-inaccessible proteins, and bridge small-molecule drugs and biological agents. As key protein phosphorylation enzymes, kinases are linked to tumors, COPD, etc., and are critical therapeutic targets. Traditional small-molecule kinase inhibitors lack selectivity, causing off-target toxicity, low bioavailability, and acquired resistance. Macrocycles’ semi-rigid structure restricts conformations, boosts binding selectivity, optimizes pharmacokinetics, and makes macrocyclization a core kinase inhibitor optimization strategy.

Thousands of bioactive macrocycles were curated from ChEMBL. Via Transformer, macrocyclization was converted into a chemical language translation task, enabling end-to-end macrocycle generation from linear precursors with simplified inputs. Macformer achieves efficient, automated linear molecule macrocyclization via deep learning; generated macrocycles have diversity, novelty, biocompatibility, and cover broader chemical space.

MCE collected thousands of marketed/clinical kinase inhibitors, using their fragments for macrocyclization to generate derivatives. After evaluating synthetic accessibility and physicochemical properties, a million-scale virtual macrocyclic library was built for kinase-related virtual and AI-driven screening.