pharmacokinetic optimization

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.

Chirality can significantly influence drug–target affinity, subtype selectivity, and toxicological profiles. The thalidomide tragedy highlights the profound biological consequences of stereochemistry, making stereochemical evaluation essential in early drug discovery. Three Nobel Prizes in Chemistry—in 2001, 2021, and 2026—have advanced our understanding of chirality, from asymmetric catalysis and stereoselective synthesis to asymmetric autocatalysis and nonlinear effects that explain the propagation and amplification of chiral signals.

Enantiomers (R/S) can exhibit distinct target-binding affinities, subtype selectivity, and pharmacokinetic properties. This library supports enantiomer-specific binding studies using affinity selection–mass spectrometry (AS-MS) and other screening platforms.

Library Highlights: Comprising 2,792 structurally diverse small molecules, the library offers broad chemical space coverage and lead-like physicochemical properties. It supports high-throughput screening across therapeutic targets, hit identification, and preliminary structure–activity relationship (SAR) studies. Complementary stereochemical characterization services facilitate seamless integration into lead optimization programs.

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.