SPACE peptide

Cyclic peptide library have advantages such as high affinity, high selectivity, and suitability for targeting protein–protein interactions. Through DEL synthesis technology, the library size can achieve hundreds of millions. DEL cyclic peptide library have advantages like low cost andhigh screeing efficiency, making them valuable for discovering lead compounds against challenging drug targets.

This cyclic peptide library is constructed with unnatural amino acids as building block, synthesized through DNA-compatible chemical reactions. Each cyclic peptide consist of six amino acids and constrained conformations such as side-chain cross-linking, disulfide bonds, and macrocyclization. These cyclic peptides exhibit significantly improved stability and druggability compared with linear peptides, filling the gap between small molecules and macromolecular biologics. Each cyclic peptide is uniquely conjugated to a DNA barcode sequence for molecular identification and sequencing decoding.

MCE’s cyclic peptide library has8 independent sub-libraries, with a total molecular diversity of 1.2 billion. It is constructed via multi-round combinatorial assembly of building blocks and diverse cyclization strategies, facilitating the discovery of cyclic peptide leads for undruggable targets.

Unnatural amino acids (UAAs), also referred to as non-canonical amino acids (ncAAs) or non-proteinogenic amino acids, are a class of amino acids that are distinct from the 20 standard natural amino acids. They can be obtained through chemical synthesis, biosynthesis, and other approaches, with structural diversity far exceeding that of natural amino acids. UAAs are mainly including naturally occurring non-canonical amino acids, chemically synthesized amino acids, and biosynthetic amino acids, which provide a molecular basis for protein function design.

UAAs exhibit significant value in multiple fields. They can optimize the pharmacokinetic properties of peptide drugs and peptidomimetics, modify enzyme functions and endow them with new biological activities, thereby overcoming the limitations of traditional peptide drugs and expanding the chemical space . Meanwhile, UAAs can serve as molecular probes to analyze protein-protein interactions and investigate the regulatory mechanisms of protein functions.

MCE has compiled a UAAs Fragment Library comprising nearly a thousand unnatural amino acid fragments with extensive coverage of chemical space and enhanced structural diversity. This compound library can be widely applied in peptide synthesis, drug design, and protein engineering.

Peptidomimetics are compounds whose essential elements (pharmacophore) mimic a natural peptide or protein in 3D space and which retain the ability to interact with the biological target and produce the same biological effect. Peptidomimetics are designed to circumvent some of the problems associated with a natural peptide: e.g. stability against proteolysis (duration of activity) and poor bioavailability. Certain other properties, such as receptor selectivity or potency, often can be substantially improved. The design and synthesis of peptidomimetics are most important because of the dominant position peptide and protein-protein interactions play in molecular recognition and signaling, especially in living systems. Hence mimics have great potential in drug discovery.

MCE Peptidomimetic Library contains 370 compounds including peptoid, α-helix mimetics, β-turn/sheets mimetics, etc. This library is an indispensable tool of structure-activity relationships in drug discovery.

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.