rigid structure

In contrast to the high conservation of conventional orthosteric sites, allosteric sites possess structural characteristics of low conservation, high hydrophobicity, weak polarity, confined spatial geometry, and dynamic cryptic properties. There is a significant difference between their core structures and orthosteric pockets — allosteric pockets are mostly dynamic grooves formed by protein conformational changes, subunit interface clefts, or shallow depressions, rather than the rigid "keyhole" structure of orthosteric sites. With looser spatial constraints, allosteric sites have the advantages of high selectivity and low off-target risk, and have become an important direction in new drug discovery.

Based on the dynamic, hydrophobic, and narrow-long spatial characteristics of allosteric pockets, MCE has performed targeted modification and screening of fragments. The screening criteria strictly conform to the requirements of allosteric binding: molecular weight is controlled at 120–280 Da (to meet the core needs of small molecules in fragment libraries and high derivatization), hydrogen bond donors (HBD ≤ 2), hydrogen bond acceptors (HBA ≤ 3), polar surface area (PSA = 30–80 Ų), rotatable bonds (≤ 2), moderate hydrophobicity (cLogP = 1–3.5), no strongly ionizable groups, and both appropriate rigidity and conformational flexibility to adapt to the dynamic changes of the pocket. Meanwhile, combined with the results of principal moment of inertia (PMI) analysis, fragments with high 3D diversity were obtained. Such fragments have good shape complementarity with allosteric pockets, ensuring that the fragments can smoothly enter the allosteric pockets and form stable binding, while providing room for subsequent optimization and derivation.

This library contains 1,800 structurally diverse fragment molecules with excellent drug-like properties, suitable for allosteric drug development and the design and optimization of allosteric sites. It combines the

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.

Owing to the high conservation of orthosteric sites, conventional orthosteric drugs frequently suffer from poor subtype selectivity, off-target toxicity, and drug resistance, severely restricting their clinical application. In contrast, allosteric sites feature low conservation, high hydrophobicity, weak polarity, confined spatial geometry, and dynamic cryptic properties. These characteristics endow allosteric modulators with distinct advantages including high selectivity, functional tunability, and improved safety, making allosteric therapy a key direction in modern drug discovery.

MCE has curated nearly 1,000 structurally disclosed clinical-stage allosteric modulators. By analyzing allosteric protein–ligand complex structures from the PDB database, we extracted core pharmacophores and privileged scaffolds. Adopting a rational design strategy of “scaffold derivation + allosteric physicochemical filtering”, we performed secondary screening on the derived compounds strictly following the optimal physicochemical principles for allosteric binding based on universal allosteric pocket properties: molecular weight 300–500 Da, HBD ≤ 3, HBA = 3–8, PSA = 70–120 Ų, rotatable bonds ≤ 6, highly rigid scaffolds, cLogP = 1.0–3.8, and no strongly ionizable groups. The selected compounds exhibit high rigidity and shape complementarity, making them well-suited for targeting shallow, dynamic, and hydrophobic-dominated allosteric pockets.

This allosteric modulator library contains 4,315 structurally diverse, lead-like compounds dedicated to allosteric drug development, allosteric site targeting, and allosteric modulator screening. It is suitable for kinases, GPCRs, and other important drug targets. All compounds are analogs of clinical-stage allosteric modulators with a similarity score > 0.6, combining excellent druggability and allosteric binding potential. It provides a highly efficient tool for early-stage allosteric drug discovery.