mARC enzyme system

With the aging population and increasing competitive pressures, neurodegenerative diseases of the central nervous system (CNS) have become a serious medical challenge in modern society, including Parkinson's disease, Alzheimer's disease, brain tumors, and multiple sclerosis. However, the success rate of CNS drug development remains remarkably low, primarily due to the blood-brain barrier (BBB). The blood-brain barrier (BBB) is a semipermeable barrier structure that surrounds the microvasculature of the CNS. In capillaries, the wedged endothelial cells are tightly packed and wedge-shaped, lining the interior of the vessels to form extensive tight junctions. Along with a range of receptors, transporters, efflux pumps, and other cellular components, this barrier regulates the entry and exit of molecules between the bloodstream and the brain. The intact BBB blocks the passage of most blood-borne substances into the brain, preventing nearly 100% of large-molecule drugs and over 98% of small-molecule drugs from entering. Compared to non-CNS drugs, physicochemical properties such as hydrogen bonds, lipophilicity, and molecular weight significantly influence a compound's ability to cross the BBB. Using artificial intelligence (AI) algorithms to predict BBB permeability, a predicted value greater than 0.75 indicates that the compound has strong potential to cross the BBB, providing a promising starting point for CNS drug discovery.

Protein protein interactions (PPI) have pivotal roles in life processes. The studies showed that aberrant PPI are associated with various diseases, including cancer, infectious diseases, and neurodegenerative diseases. The classic drug targets are usually enzymes, ion channels, or receptors, the PPI indicate new potential therapeutic targets. Therefore, targeting PPI is a new direction in treating diseases and an essential strategy for the development of new drugs.

However, the design of modulators targeting PPI still faces tremendous challenges, such the difficult PPI interfaces for the drug design, lack of ligands reference, lack of guidance rules for the PPI modulators development and high-resolution PPI proteins structures.

With the development of high-throughput technology, high-throughput screening is also gradually used for the identification of PPI inhibitors, but the compound library used for conventional target screening is not very effective in screening PPI inhibitors. To improve screening efficiency, MCE carefully selected 826 PPI inhibitors and mainly targeting MDM2-p53, Keap1-Nrf2, PD-1/PD-L1, Myc-Max, etc. MCE Protein-protein Interaction Inhibitor Library is a useful tool for PPI drug discovery and related research.

In the progression of various diseases, metabolic reprogramming has emerged as a key hallmark. Lactate, as an important metabolic signaling molecule, is widely involved in tumorigenesis, immune regulation, and inflammatory responses. Particularly within the tumor microenvironment, the abnormal accumulation of lactate not only affects cellular energy metabolism but also promotes disease progression by modulating immune cell functions and mediating protein lactylation, thereby participating in epigenetic regulation and signaling networks. Therefore, systematic investigation of lactate metabolic pathways and their associated metabolites is of great significance for understanding disease mechanisms and developing novel therapeutic strategies.

The MCE lactic acid metabolite compound library contains 61 compounds and is constructed around key metabolic pathways involving lactate production, transport, and utilization. This library systematically includes core intermediates from glycolysis, the tricarboxylic acid (TCA) cycle, and the lactate cycle. Focusing on disease-associated metabolic reprogramming, it is suitable for research in oncology, inflammation, and metabolic disorders. The library can be used to elucidate the roles of lactate in tumor microenvironment regulation, immune evasion, and epigenetic modifications (such as protein lactylation). In addition, it provides high-quality small-molecule resources for drug screening, facilitating the discovery of potential modulators targeting key enzymes (such as LDH) or transporters (such as MCTs) involved in lactate metabolism.

Small molecule covalent inhibitors, or irreversible inhibitors, are a type of inhibitors that exert their biological functions by irreversibly binding to target through covalent bonds. Compared with non-covalent inhibitors, covalent inhibitors have obvious advantages in bioactivity, such that covalent warheads can target rare residues of a particular target protein, thus leading to the development of highly selective inhibitors and achieving a more complete and continued target occupancy in living systems. In recent years, the distinct strengths of covalent inhibitors in overcoming drug resistance had been recognized. However, toxicity can be a real challenge related to this class of therapeutics due to their potential for off-target reactivity and has led to these drugs being disfavored as a drug class. The drug design and optimization of covalent inhibitors has become a hot spot in drug discovery.

MCE covalent inhibitor library contains 6,121 small molecules including identified covalent inhibitors and other molecules having common covalent reactive groups as warheads, such as acrylamides, activated terminal acetylenes, sulfonyl fluorides/esters, cloracetamides, alkyl halides, epoxides, aziridines, disulfides, etc.

MCE Covalent inhibitor Library plus, with more powerful screening capability, further complement Covalent inhibitor Library (HY-L036) by adding some fragment compounds with covalent warheads.

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.

Glutamine is an important metabolic fuel that helps rapidly proliferating cells meet the increased demand for ATP, biosynthetic precursors, and reducing agents. Glutamine Metabolism pathway involves the initial deamination of glutamine by glutaminase(GLS), yielding glutamate and ammonia. Glutamate is converted to the TCA cycle intermediate α-ketoglutarate (α-KG) by either glutamate dehydrogenase (GDH) or by the alanine or aspartate transaminases (TAs), to produce both ATP and anabolic carbons for the synthesis of amino acids, nucleotides and lipids. During periods of hypoxia or mitochondrial dysfunction, α-KG can be converted to citrate in a reductive carboxylation reaction catalyzed by IDH2. The newly formed citrate exits the mitochondria where it is used to synthesize fatty acids and amino acids and produce the reducing agent, NADPH.

Cancer cells display an altered metabolic circuitry that is directly regulated by oncogenic mutations and loss of tumor suppressors. Mounting evidence indicates that altered glutamine metabolism in cancer cells has critical roles in supporting macromolecule biosynthesis, regulating signaling pathways, and maintaining redox homeostasis, all of which contribute to cancer cell proliferation and survival. Thus, intervention in glutamine metabolic processes could provide novel approaches to improve cancer treatment.

MCE owns a unique collection of 1,827 compounds targeting the mainly proteins and enzymes involved in glutamine metabolism pathway. Glutamine Metabolism compound library is a useful tool for intervention in glutamine metabolic processes.