spacers

Glycomimetics are designed to mimic the structure of natural carbohydrates and modulate their disease-related functions. Macrocyclic glycomimetics are an extremely interesting class of glycomimetics as they occupy space between small and macro molecules. Macrocyclic glycomimetics are mostly represented by naturally occurring molecules derived from marine microorganisms and bacterial or fungal metabolites.

MCE 50K Diversity Library consists of 50,000 lead-like compounds with multiple characteristics such as calculated good solubility (-3.2 < logP < 5), oral bioavailability (RotB <= 10), drug transportability (PSA < 120). These compounds were selected by dissimilarity search with an average Tanimoto Coefficient of 0.52. There are 36,857 unique scaffolds and each scaffold 1 to 7 compounds. What’s more, compounds with the same scaffold have as many functional groups as possible, which make abundant chemical spaces. This exceptionally diverse library is highly recommended for random screening against new as well as popular targets based its novel, diverse scaffolds, abundant chemical spaces and the convenience for subsequent modification.

Designed to maximize efficiency in hit discovery and optimization, this compound library is built on a foundation of diverse Bemis-Murcko scaffolds, with each scaffold is represented by two specifically derived molecules. This strategy ensures broad chemical space through scaffold diversity while enabling preliminary functional group exploration. This approach provides early structure-activity relationship (SAR) insights for every scaffold, making it a valuable tool for accelerating drug discovery.

The MCE 1K Drug Fragment Library consists of 1,366 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).

Unnatural amino acids (UAAs), also referred to as non-canonical amino acids (ncAAs) or non-proteinogenic amino acids, are a class of amino acids that are distinct from the 20 standard natural amino acids. They can be obtained through chemical synthesis, biosynthesis, and other approaches, with structural diversity far exceeding that of natural amino acids. UAAs are mainly including naturally occurring non-canonical amino acids, chemically synthesized amino acids, and biosynthetic amino acids, which provide a molecular basis for protein function design.

UAAs exhibit significant value in multiple fields. They can optimize the pharmacokinetic properties of peptide drugs and peptidomimetics, modify enzyme functions and endow them with new biological activities, thereby overcoming the limitations of traditional peptide drugs and expanding the chemical space . Meanwhile, UAAs can serve as molecular probes to analyze protein-protein interactions and investigate the regulatory mechanisms of protein functions.

MCE has compiled a UAAs Fragment Library comprising nearly a thousand unnatural amino acid fragments with extensive coverage of chemical space and enhanced structural diversity. This compound library can be widely applied in peptide synthesis, drug design, and protein engineering.

Natural products are an attractive source with varied structures that exhibit potent biological activities, and desirable pharmacological profiles. The core scaffold of a natural product can also provide a biologically validated framework upon which to display diverse functional groups. Inspired by bioactive natural products, natural product-like compounds, occupying the same chemical space, are ideally suited to explore and to facilitate understanding of biological pathways.

MCE provides a unique collection of 45 natural product-like compounds that are structurally like Steroids, Tannins, Flavonoids, Quinones, Isoquinolines, etc. This library is an important source of lead compounds for drug discovery.

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 2,258 high-structurally diverse 'RO3' compliant fragment molecules from large-scale fragment molecules, which can be applied to fragment based drug development.

Sulfonyl fluoride (-SO₂F) overcomes the bottleneck of target selectivity in traditional covalent warheads through its unique chemical and biological properties, which rely heavily on cysteine (Cys) residues. Featuring high stability and tunable electrophilicity under physiological conditions, it can target a wide range of nucleophilic residues including lysine (Lys), tyrosine (Tyr), serine (Ser), and histidine (His). It offers the advantages of a broader druggable space, lower off-target risks, and long-lasting efficacy, with numerous reported cases in the research of covalent inhibitors, Molecular glue, PROTACs, and chemical biology probe development.

MCE constructs a highly diverse sulfonyl fluoride fragment library based on the reactivity, stability and physiological compatibility of sulfonyl fluoride. The library contains 1000 efficiently synthesized and stable sulfonyl fluoride fragments, which ensure precise reactivity of the warhead and retain sufficient derivatization space for subsequent optimization. Combined with the modular strategy of SuFEx click chemistry, it enables versatile modification of compounds and functionalization of complex molecules, improves the efficiency of structural optimization and rapidly expands druggability, making it suitable for high-throughput probe and custom covalent library construction. It provides an efficient research tool for the development of broad-spectrum covalent inhibitors targeting Lys/Tyr/Ser/His, covalent PROTACs for E3 ligases and chemical biology probe development, meeting the requirements of modern drug research for high throughput, high success rate and high derivatization potential.

This library contains 1,162 sulfonyl fluoride fragments with high structural diversity, favorable drug-like properties and tunable electrophilicity. It is well suited for precise targeting of non-Cys residues and meets the criteria of simple structure and high derivatization potential. It effectively improves

Peptidomimetics are compounds whose essential elements (pharmacophore) mimic a natural peptide or protein in 3D space and which retain the ability to interact with the biological target and produce the same biological effect. Peptidomimetics are designed to circumvent some of the problems associated with a natural peptide: e.g. stability against proteolysis (duration of activity) and poor bioavailability. Certain other properties, such as receptor selectivity or potency, often can be substantially improved. The design and synthesis of peptidomimetics are most important because of the dominant position peptide and protein-protein interactions play in molecular recognition and signaling, especially in living systems. Hence mimics have great potential in drug discovery.

MCE Peptidomimetic Library contains 370 compounds including peptoid, α-helix mimetics, β-turn/sheets mimetics, etc. This library is an indispensable tool of structure-activity relationships in drug discovery.

DEL technology enables the simultaneous screening of millions or billions of compounds in a single tube by covalently linking each small molecule with a unique DNA sequence. Traditional DEL screening primarily focuses on identifying non-covalent binding molecules, where interactions with the target are reversible. In contrast, DNA‑encoded covalent library is an ultra‑high‑throughput screening library developed on the basis of conventional DNA‑encoded library technology. It incorporates controllable electrophilic covalent warheads capable of forming irreversible covalent bonds with amino acid residues at the active sites of target proteins, including Cys, Lys, Ser, Tyr, and others. This covalent binding enhances binding affinity, prolongs residence time at the target site, and has the potential to overcome challenges associated with traditional non-covalent inhibitors, such as drug resistance or off-target effects.

Each compound in the library contains both a binding domain and an electrophilic warhead. It first recognizes and binds to the target through non covalent interactions, and then forms a stable covalent bond with key amino acid residues to achieve irreversible inhibition. This library is specifically designed for the discovery of potent, long lasting, and highly selective covalent inhibitors, particularly for undruggable targets such as kinases, GPCRs, proteases, and mutant oncoproteins. Each molecule is uniquely labeled with a DNA barcode for molecular identification and sequencing decoding.

This library is an advanced and highly diverse collection, consists of 35 independent sub-libraries with a total scaleof 14 million compounds, It incorporates over 14 experimentally validated covalent warheads capable of targeting cysteine, lysine, arginine, aspartic acid and glutamic acid. This library is constructed with diverse drug like core scaffolds and integrated controllable covalent warheads, it features structural diversity, reaction spec

Natural products are an attractive source with varied structures that exhibit potent biological activities, and desirable pharmacological profiles. The core scaffold of a natural product can also provide a biologically validated framework upon which to display diverse functional groups. Inspired by bioactive natural products, natural product-like compounds, occupying the same chemical space, are ideally suited to explore and to facilitate understanding of biological pathways.

MCE 10K Natural Product-like Compound Library consists of 10,000 natural product-like compounds. Each compound has scaffold of natural products or Tanimoto coefficient >0.6 with natural products. The natural-likeness scoring of these compounds is >-2. What’s more, compounds in the library are drug-like and readily available for re-supply, making it a powerful tool for new drug research and development. It can be widely applied in high-throughput screening (HTS) and high-content screening (HCS).

Natural products are an attractive source with varied structures that exhibit potent biological activities, and desirable pharmacological profiles. The core scaffold of a natural product can also provide a biologically validated framework upon which to display diverse functional groups. Inspired by bioactive natural products, natural product-like compounds, occupying the same chemical space, are ideally suited to explore and to facilitate understanding of biological pathways.

MCE 5K Natural Product-like Compound Library consists of 5,000 natural product-like compounds. Each compound has scaffold of natural products or Tanimoto coefficient >0.6 with natural products. The natural-likeness scoring of these compounds is >-2. What’s more, compounds in the library are drug-like and readily available for re-supply, making it a powerful tool for new drug research and development. It can be widely applied in high-throughput screening (HTS) and high-content screening (HCS).

Macrocycles, molecules containing 12-membered or larger rings, are receiving increased attention in small-molecule drug discovery. The reasons are several, including providing access to novel chemical space, challenging new protein targets, showing favorable ADME- and PK-properties. Macrocycles have demonstrated repeated success when addressing targets that have proved to be highly challenging for standard small-molecule drug discovery, especially in modulating macromolecular processes such as protein–protein interactions (PPI). Otherwise, the size and complexity of macrocyclic compounds make possible to ensure numerous and spatially distributed binding interactions, thereby increasing both binding affinity and selectivity.

MCE offers a unique collection of 447 macrocyclic compounds which can be used for drug discovery for high throughput screening (HTS) and high content screening (HCS). MCE Macrocyclic Compound Library is a useful tool for discovering new drugs, especially for “undruggable” targets and protein–protein interactions.

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.

Cyclic peptide library have advantages such as high affinity, high selectivity, and suitability for targeting protein–protein interactions. Through DEL synthesis technology, the library size can achieve hundreds of millions. DEL cyclic peptide library have advantages like low cost andhigh screeing efficiency, making them valuable for discovering lead compounds against challenging drug targets.

This cyclic peptide library is constructed with unnatural amino acids as building block, synthesized through DNA-compatible chemical reactions. Each cyclic peptide consist of six amino acids and constrained conformations such as side-chain cross-linking, disulfide bonds, and macrocyclization. These cyclic peptides exhibit significantly improved stability and druggability compared with linear peptides, filling the gap between small molecules and macromolecular biologics. Each cyclic peptide is uniquely conjugated to a DNA barcode sequence for molecular identification and sequencing decoding.

MCE’s cyclic peptide library has8 independent sub-libraries, with a total molecular diversity of 1.2 billion. It is constructed via multi-round combinatorial assembly of building blocks and diverse cyclization strategies, facilitating the discovery of cyclic peptide leads for undruggable targets.

RNA is crucial for the regulation of numerous cellular processes and functions. With the in-depth study of disease mechanisms, processes such as RNA expression, splicing, translation, and stability regulation have become new targets for disease intervention. RNA has provided new therapeutic modalities for metabolic diseases, genetic disorders, and cancer patients, resulting in several innovative drugs.

MCE R&D team collected small molecules targeting RNA from the PDB, R-BIND, ROBIN, and internal database as the positive dataset, and non-targeting RNA small molecules from ROBIN as the negative dataset. Based on the GeminiMol pre-trained model, we encoded the molecules and calculated over 1700 molecular descriptors using Mordred as inputs for the model. Subsequently, we employed 13 deep learning models to learn from the data. All of which yielded good training results, with AUROCs greater than 0.75. Ultimately, we selected the Finetune model to screen HY-L901P, which exhibited the best classification performance, achieving an AUROC of 0.82 and a prediction accuracy of 0.76. We then applied filtering based on StaR rules (with at least two of the following properties: cLogP ≥ 1.5, Molar Refractivity ≥ 4, Relative Polar Surface Area ≤ 0.3) to obtain a library containing approximately 5,000 small molecule compounds targeting RNA. This library serves as a valuable tool for screening small molecules that interact with RNA.

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.