Linker 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.

POI (Protein of Interest) refers to the target protein, namely the disease-causing protein or key functional protein that undergoes degradation or functional modulation in molecular glue-mediated processes. The Molecular Glue POI Library consists of a series of fragments that can specifically bind to different types of POIs. As key components of molecular glues, these ligands form stable interactions with target proteins, laying the foundation for molecular glues to induce the interaction between POIs and E3 ubiquitin ligases. The covered POIs include various types such as cancer-associated GSPT1, androgen receptors, and abnormally aggregated proteins linked to neurodegenerative diseases.

This fragment library can be applied to the screening and optimization of targeted protein degraders. By screening ligands with high affinity and strong selectivity for specific POIs from the library, core structures can be identified to develop novel molecular glues. For instance, optimization of ligands targeting GSPT1 has yielded molecular glue degraders with enhanced degradation activity. Since many POIs are difficult to drug due to the lack of traditional small-molecule binding pockets, some ligands in the POI Ligand Library can modulate such POIs by inducing protein-protein interactions, thereby further expanding the scope of drug discovery for undruggable targets.

MCE has compiled a POI Fragment Library comprising thousands of POI fragments with molecular weights ranging from 150 to 400. This compound library can be widely applied in Molecular Glue research and development.

Targeted Protein Degradation (TPD) is a novel and promising approach to drug development. It shows great potential for targeting proteins traditionally considered "undruggable" due to the lack of enzymatic function and absence of binding sites by tagging them for degradation or recruiting natural degradation mechanisms.

Molecular glues are a type of small-molecule degraders that primarily induce novel interactions between E3 ubiquitin ligases and target proteins, forming ternary complexes that lead to protein ubiquitination and subsequent proteasomal degradation. Compared with PROTACs, molecular glues generally have lower molecular weights, higher cell permeability, and better drug-like properties. Additionally, the design of molecular glues is relatively simple, without the requirements for complex linkers and ligand optimization. As a result, molecular glues have gradually emerged as a promising therapeutic approach for various diseases.

Multiple types of molecular glues have been reported previously. Analysis of co-crystal complex structures reveals that CRBN-related molecular glues are more versatile. Therefore, MCE researchers select active molecules related to these targets as probes for artificial intelligence (AI) screening.Subsequently, molecular docking technology was used to verify whether the screened molecules retained the key pharmacophore features. Ultimately, we obtained 317 molecular glue analogs, and these compounds serve as powerful tools for the research of molecular glues.