Molecular Weight

19F-NMR has proved to be a detection mode in fragment-based drug discovery (FBDD) for studies of protein structure and interactions. 19F shows high sensitivity for NMR detection, and the exquisite sensitivity of 19F chemical shifts and linewidths to ligand binding all make it a valuable approach in FBDD.F (Fluorine) -Fragments can be used for 19F-NMR detection after binding to target proteins, and can be used as an effective 19F-NMR tool for FBDD.

MCE designs a unique collection of 5,134 F-fragments, all of which obey a heuristic rule called the “Rule of Three (RO3)”, in which molecular weight ≤300 Da, the number of hydrogen bond donors (H-donors) ≤3, the number of hydrogen bond acceptors (H-acceptors) is ≤3 and cLogP is ≤3. This F-fragments library is an important source of lead-like drugs.

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. Fragment-based drug discovery is well-established in industry and has resulted in a variety of drugs entering clinical trials, with two, vemurafenib and venetoclax, already approved. FBDD also has key attractions for academia. Notably, it is able to tackle difficult or novel targets for which no chemical matter may be found in existing HTS collections.

MCE designs a unique collection of 23,342 fragment compounds, all of which obey a heuristic rule called the “Rule of Three (RO3) ”, in which molecular weight ≤300 Da, the number of hydrogen bond donors (H-donors) ≤3, the number of hydrogen bond acceptors (H-acceptors) is ≤3 and cLogP is ≤3. This library is an important source of lead-like drugs.

Fragment-based drug discovery (FBDD) offers a strategic advantage by categorizing fragment hits according to their functional groups. This approach facilitates both the further optimization of these hits and the rational design of larger compounds through fragment combination. The amine functional group plays a vital role in drug development, as evidenced by its presence in many marketed drugs like Galantamine, Tacrine, and Rivastigmine. It is instrumental in enhancing solubility, improving bioavailability, and ensuring shelf-life stability—all critical factors for drug efficacy.

MCE offers a collection of 19,593 amine fragments for drug discovery. All of these compounds adhere to the Rule of Three (RO3) criteria for drug-likeness, which MCE offers a collection of 19,593 amine fragments for drug discovery, all of which stipulates a molecular weight ≤ 300 Da, ≤ 3 hydrogen bond donors, ≤ 3 hydrogen bond acceptors, and a cLogP ≤ 3.

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.

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.

CRBN, namely cereblon, is the substrate recognition subunit of the E3 ubiquitin ligase complex in the ubiquitin-proteasome system. A CRBN ligand library refers to a collection of numerous fragments that can specifically bind to the CRBN protein.

These ligands are mostly designed based on validated CRBN-binding warheads and modified through AI-driven molecular generation optimization systems. They not only include classic lenalidomide-derived structures but also cover novel non-lenalidomide scaffolds. After drug-likeness filtering, these ligands exhibit structural diversity and favorable druggable properties. They can be further optimized and modified to facilitate the development of novel molecular glue degraders, accelerate the discovery of molecular glues that induce interactions between CRBN and new substrate proteins, and enable the exploration of novel CRBN substrates for identifying previously unknown CRBN-binding proteins.

MCE compiles 121 fragments that can specifically bind to the CRBN protein, with molecular weights ranging from 200 to 500. Compounds developed based on the library ligands target multiple disease targets such as cancer and autoimmune diseases, further advancing the development of Molecular Glues and PROTACs therapeutic agents.

Metabolomics, positioned as the systemic characterization of small-molecule metabolites within biological systems, has emerged as an indispensable analytical platform in both fundamental research and translational applications across plant sciences, microbial biotechnology, and biomedical investigations. Functioning as a critical component in multi-omics integration, this discipline deciphers the intricate molecular networks operating downstream of genomic, transcriptomic, and proteomic regulation, thereby capturing the dynamic biochemical phenotype closest to organismal functionality. The metabolome, comprising endogenous compounds with molecular weights typically below 1500 Da, serves as the functional readout of cellular processes and environmental interactions, where perturbations in metabolic networks are frequently implicated in disease pathogenesis. Such unique attributes have propelled metabolomics into a pivotal role in pharmacological research, particularly in target deconvolution, pharmacodynamic assessment, and mechanistic elucidation of pathological processes.

MCE can provide 5,955 mass spectrometry human metabolites that can be used for metabolite identification and quantification, functional cell detection and phenotypic screening of mass spectrometry.

Polysaccharides are long chains of carbohydrate molecules, consisting of multiple smaller monosaccharides. Polysaccharides are found mainly in natural sources such as plants, microorganisms, algae and animals. Polysaccharides have a large number of active functional groups, different chemical compositions and different molecular weight ranges, which determines their diversity in nature and structure. Also in the field of medical research, polysaccharides act as a class of functional compounds and thus play a role. For example, nanocarrier construction, immunomodulation and vaccine development, new strategies for antitumor therapy, tissue regeneration engineering applications and disease diagnosis. With the advancement of glycomics and synthetic biotechnology, human beings are moving from “knowing polysaccharides” to “designing polysaccharides”, which will provide innovative solutions for materials science, precision medicine and sustainable development.

MCE offers 67 polysaccharides that can be used in biomedical studies.

Fragment-based drug development (FBDD) is a strategy for drug discovery that can be applied both academically and commercially to enhance the identification of some non-drug targets. Fragment-based drug development has identified low molecular weight molecules (<300 Da) capable of binding to related macromolecules. These fragments can cover a wide chemical space and are easy to optimize later. Currently, several fragment-based drugs have entered clinical trials, of which two drugs, Vemurafenib and Venetoclax, have been approved for marketing.

Based on Tanimoto coefficient, MCE uses similarity algorithm to carefully select 2,250 high-structurally diverse 'RO3' compliant fragment molecules from large-scale fragment molecules, which can be applied to fragment based drug development.

The MCE 1K Drug Fragment Library consists of 1,369 drug fragments. These drug fragments are derived from 2,946 FDA-approved drug molecules, and fragments from one drug can appear in other drugs, so these fragments are somewhat correlated with good PK/PD properties. Fragment-based screening can reserve enough chemical space for subsequent structural optimization. This compound library is an essential tool for drug screening based on FBDD (Fragment-Based Drug Discovery).

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.

Cosmetics are complex mixtures formulated through the rational blending and processing of various natural, synthetic, or extracted raw materials. The ingredients used in cosmetics are diverse in type and functionality. Based on their properties and applications, cosmetic ingredients can generally be classified into two main categories: base materials and auxiliary ingredients. The former constitutes the primary component of cosmetics, accounting for a significant proportion in formulations and serving as the key functional substances. Auxiliary ingredients, on the other hand, play roles in shaping the product, ensuring stability, or imparting color, fragrance, and other specific characteristics.Establishing a systematic ingredient database is of great significance for cosmetic research and development. By analyzing the physicochemical properties (such as oil-water partition coefficients and molecular weight distribution), biological activity mechanisms (such as antioxidant pathways and cellular signaling regulation), and compatibility of ingredients, the database can provide precise guidance for formulation design. This approach helps shorten the development cycle of cosmetic/skincare products and reduces trial-and-error costs.

MCE offers 624 types of cosmetic ingredient compounds, including antioxidants, humectants, emulsifiers, film-forming agents, and more.

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.

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

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.

MCE Weigert Elastin Staining Kit combines Weigert Resorcinol Fuchsin Staining Solution with Van Gieson (VG) Staining Solution. Weigert Resorcinol Fuchsin Solution is mainly used for staining elastic fibers, Van Gieson (VG) Solution is used for collagen fiber staining. The principle of VG staining is based on differences in the size of anionic dye molecules and the permeability of tissues. Picric acid (PA) has the smallest molecular weight and preferentially enters dense structures. Ponceau Red or acid fuchsin has a relatively larger molecular weight and primarily binds to collagen fibers, while light green has the largest molecular weight and stains other tissue components. After VG staining, collagen fibers appear red, while muscle fibers and cytoplasm appear yellow, enabling a clear distinction between tissue components.

Protein Deglycosylation Kit (for N-Linked & Simple O-Linked Glycans) includes the enzymes and reagents necessary for the removal of all N-linked and relatively simple O-linked glycans. The enzymatic reagents are a mixture of three glycosidases, namely PNGase F, O-Glycosidase, and α2-3,6,8,9 Neuraminidase. All enzymes are recombinant enzymes expressed in Escherichia coli BL21 and subsequently purified. The molecular weights of these enzymes are as follows: PNGase F is 36 kDa, O-Glycosidase is 147 kDa, and α2-3,6,8,9 Neuraminidase is 66 kDa.

Protein Deglycosylation Kit (for N-linked & Complex O-linked Glycans) includes the enzymes and reagents reuired for the removal of all N-linked and some complex O-linked glycans. The enzyme reagent in this kit is a mixture of five glycosidases, namely PNGase F, O-Glycosidase, α2-3,6,8,9 Neuraminidase, β1-4 Galactosidase, and β-N-Acetylhexosaminidase. All enzymes in this kit are recombinant enzymes expressed in Escherichia coli BL21 and subsequently purified. The molecular weights of these enzymes are as follows: PNGase F is 36 kDa, O-Glycosidase is 14 kDa,α2-3,6,8,9 Neuraminidase is 66 kDa, β1-4 Galactosidase is 94 kDa, and β-N-Acetylhexosaminidase is 55 kDa.