rigid structure

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.

Spirocyclic compounds, with rigid 3D structures, high Fsp³ and strong conformational restriction, are highly privileged scaffolds in small-molecule drug screening. They overcome drawbacks of planar aromatic compounds such as poor solubility, high off-target risks and weak druggability. Their orthogonal bicyclic geometry fits well into protein pockets, improving target affinity, subtype selectivity, metabolic stability and membrane permeability, making them ideal for hit identification against kinases, GPCRs, PPIs and other targets.

Spirocyclic scaffolds have been widely applied in oncology, antivirals, hypertension and CNS diseases, leading to many approved drugs and clinical candidates. SAR studies show that spiro-atom chirality, ring size and heteroatom substitution dominate bioactivity and selectivity, with the scaffold mainly serving as a conformational anchor. Azaspirocycles, spirooxindoles and spirosteranes target GPCRs, kinases, MDM2-p53 and PPIs. Approved drugs including irbesartan, spironolactone and rolapitant confirm their druggability, while revumenib and SAR405838 show promise against undruggable targets.

The MCE Spirocyclic Druglike Library contains over 1,000 diverse, stereospecific molecules selected by Lipinski’s rules. It covers privileged cores such as azaspirocycles, oxaspirocycles and spirooxindoles. These molecules bear rich chiral centers and distinct 3D orientations, reducing non-specific binding and enhancing screening efficiency. Featuring novel scaffolds, the library offers a highly innovative starting point for drug discovery.