macrocycle library

ASINEX has elaborated a library of diverse macrocycles using an effective tool box of synthetic methods. The resulting scaffolds are novel, tremendously diverse, medchem-relevant, macrocyclic frameworks.

Macrocyles tend to be larger than traditional screening molecules which make them perfect discovery tools for targets with shallow or extended binding sites. At the same time, their unique character based on restricted flexibility and ability to form intra-molecular hydrogen bonds allows for design approaches effectively optimizing properties such asaqueous solubility and membrane permeability. Many of these macrocycles have been tested for aqueous and DMSO solubility with cut-offs applied at 10 mM in DMSO and 50 µM in PBS (pH 7.4) followed by PAMPA permeability assay.

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.

Macrocycles, molecules containing 12-membered or larger rings, are receiving increased attention in small-molecule drug discovery. The reasons are several, including providing access to novel chemical space, challenging new protein targets, showing favorable ADME- and PK-properties. Macrocycles have demonstrated repeated success when addressing targets that have proved to be highly challenging for standard small-molecule drug discovery, especially in modulating macromolecular processes such as protein–protein interactions (PPI). Otherwise, the size and complexity of macrocyclic compounds make possible to ensure numerous and spatially distributed binding interactions, thereby increasing both binding affinity and selectivity.

MCE offers a unique collection of 461 macrocyclic compounds which can be used for drug discovery for high throughput screening (HTS) and high content screening (HCS). MCE Macrocyclic Compound Library is a useful tool for discovering new drugs, especially for “undruggable” targets and protein–protein interactions.

Glycomimetics are designed to mimic the structure of natural carbohydrates and modulate their disease-related functions. Macrocyclic glycomimetics are an extremely interesting class of glycomimetics as they occupy space between small and macro molecules. Macrocyclic glycomimetics are mostly represented by naturally occurring molecules derived from marine microorganisms and bacterial or fungal metabolites.