HIT

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.

MCE 5K Scaffold Library consists of 5,000 lead-like compounds. Each compound represents one unique scaffold. All compounds are compatible with Lipinski’s rule (Rule of 5) with multiple characteristics such as calculated good solubility (-3.2 < logP < 5), oral bioavailability (RotB <= 10), drug transportability (PSA < 120). Compounds contained within the library have been screened to remove any inappropriate chemical structures, avoiding “false hits”. The sufficient diverse of compound structure makes this library a powerful tool for drug screening.

A unique set of molecules containing mild electrophilic moieties that covalently interact with amino acid residues in the target protein. The diversity of our compounds for covalent drug discovery ranges from natural product-like scaffolds to macrocycles, creating multiple opportunities in hit generation for a selected target.

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 19,568 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 19,568 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.

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function. 3-dimensionality (3D) diversity is pivotal because the molecular shape is one of the most important factors in molecular recognition by a biomolecule. There is a developing appreciation that 3D fragments could offer opportunities that are not provided by 2D fragments.

MCE 3D Diverse Fragment Library consists of 5,400 non-flat fragment-like molecules (average Fsp3 value 0.58). More than 4,700 fragment compounds contain at least one chiral center in the structure. The key concepts that underlie the library design were 3D shape, structural diversity, reactive functionality and fragment-like. This 3D Diverse Fragment Library brings higher fragment hit optimization and increases the likelihood to find innovative hits in FBDD.

MCE-18 stands for Medicinal Chemistry Evolution 2018, which was first published in Journal of Medicinal Chemistry in 2019 for assessing molecular novelty and three-dimensional complexity. Developed based on Clarivate global pharmaceutical patent database, this descriptor was constructed via big-data analysis covering 28,161 patented lead compounds, 1,370 approved drugs and nearly 30,000 preclinical-to-phase III drug candidates from 23 top pharmaceutical companies worldwide between 1950 and 2018, followed by structural clustering and removal of redundant outdated scaffolds for data denoising. Its scoring system integrates five core structural features including aromatic ring (AR), aliphatic heterocycle (NAR), chiral center (CHIRAL), spiro atom (SPIRO), cyclic and acyclic sp³ carbon ratio together with a quadratic topological correction factor. Breaking the limitations of the single Fsp³ parameter, MCE-18 effectively distinguishes conventional flat aromatic scaffolds from modern 3D-enriched novel chemotypes, overcoming typical drawbacks of traditional compound libraries such as scaffold redundancy, low screening hit rates and poor compatibility with allosteric and PPI-related difficult targets.

This library contains over 37,000 structurally diverse compounds with favorable overall drug-likeness, suitable for high-throughput screening against canonical targets including kinases, GPCRs and proteases as well as challenging allosteric and PPI targets. Compounds comply with the developmental trend of modern novel drug discovery, supporting routine primary screening as well as early hit identification of allosteric modulators and PPI inhibitors, serving as an efficient screening resource for early-stage innovative drug discovery.

At the forefront of innovative drug discovery, every medicinal chemist faces the challenge of rapidly identifying high-quality hit compounds from vast repositories of chemical resources.

The MCE Natural Product Diversity Scaffold Library is the result of a streamlined optimization process built upon our existing natural product collection. Adhering to the rigorous selection principle of "retaining only one representative compound per BMS scaffold", we have concentrated the diversity of thousands of compounds into a high-value, low-redundancy core set containing 2,272 compounds. All compounds are derived from natural sources, inheriting their inherent advantages of structural complexity and drug-likeness. By eliminating redundancy, the library size is significantly reduced without any compromise to chemical diversity. This approach effectively lowers the cost and time required for primary screening while simplifying downstream data analysis and structure-activity relationship (SAR) studies.

The discovery of hit molecule is a cornerstone of drug development. Among the diverse tools available, DNA-encoded libraries have emerged a revolutionary platform for high-throughput screening. Compared with traditional HTS, DEL features shorter screening processes, lower costs, simpler assays, and larger library capacities.

DEL Construction utilizes split-and-pool synthesis, a combinatorial chemistry approach that involves iterative splitting, reaction, and pooling. This strategy enables rapid, exponential assembly of fragments in minimal steps without the need for individual compound synthesis andassoicicated isolation or purification steps, thus greatly reducing overall costs. The technology enables simultaneous affinity screeningof massive compound collections to target proteins in a single step. By coupling chemical structures with unique DNA barcodes, each compound is tagged with a distinct DNA sequence for convenient tracking and decoding.DELs readily enable the construction and efficient screening of libraries containing millions to billions of compounds. As a result, DEL screening combines the dual advantages of high efficiency and low cost, making DEL a transformative technology in modern drug discovery.

The DEL kit consists of 50 independent libraries with a total scale of 100 billion compounds. It is constructed through stepwise combinatorial chemistry strategies involving 2-, 3-, and 4-round synthesis. By employing diverse scaffolds and flexible linking strategies, it encompasses various ring systems, linear frameworks, and heterocyclic structures. Screening can be achieved solely through affinity, independent of target-specific activity detection methods. This library is suitable for DEL screening against a wide range of targets.

The incidence and significance of central nervous system diseases are increasing at an alarming rate all over the world. Although substantial research efforts have been applied to develop new CNS-active drugs, only a few CNS disorders are addressed satisfactorily, while the remaining ones pose significant clinical challenges. Blood-brain barrier (BBB) permeability is one of the most important limiting factors in the design and development of novel CNS-targeted pharmaceuticals for the treatment of neurological disorders.

Carefully selected from the HTS Compound Collection to meet the parameters optimized for high BBB-permeability, our CNS Focused Screening Library comprising over 30,300 structurally-diverse and potentially CNS-active screening compounds. This original Screening Compound Library is aimed at supporting CNS drug design projects and HTS efforts in search for novel neurotherapeutics.

Macrocycles are promising scaffolds for the design of novel RNA targeting molecules. This collection of macrocycles for RNA consists of very diverse, drug-like molecules which incorporate certain known RNA-recognition elements (e.g. nucleobase ring systems and analogs) distributed within macrocyclic rings or peripheral fragments. As macrocyclic molecules tend to be larger than traditional screening molecules, it is vital to carefully assess and control their physicochemical properties. All macrocycles have been tested for aqueous and DMSO solubility with cutoffs applied at 10 mM in DMSO and 50 µM in PBS (pH 7.4); PAMPA permeability has also been tested for representative set of macrocycles.

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.

On May 15, 2024, "Dimerization and antidepressant recognition at noradrenaline transporter" was published online by Nature. The research findings were an effort from Shanghai Institute of Materia Medica, Chinese Academy of Sciences. This study unraveled the important neural system target - the noradrenaline transporter (NET), obtaining the binding modes of human NET homodimers with the natural substrate norepinephrine (NE) and six selective antidepressants. It laid an important theoretical foundation for understanding the physiological regulation mechanisms of NET and other monoamine transporters.

The Norepinephrine Transporter (NET) Compound Library is obtained by computer-aided virtual screening based on the HY-L901 compound library . The specific screening process includes molecular docking screening, key pharmacophore screening, and CNS-MPO screening, which can be used for new drug discovery targeting the noradrenaline transporter.

Molecular Glue Virtual Library is constructed using generative AI technology, integrating the structural features, activity data of known molecular glues, and interaction information of ternary complexes (target protein-E3-molecular glue). Endowed with structural novelty, drug-likeness, diversity and synthesizability, it is applicable to molecular glue-based AI drug screening and large-scale virtual screening.

MCE builds this library based on high-quality molecular building blocks by virtue of robust computing power, coupled with rigorous reaction rules and optimized compound generation strategies. To ensure library quality, molecules with high synthetic difficulty, poor drug-likeness, PAINS and other undesirable molecules are excluded first. Subsequently, scaffold-based compound analysis is performed to screen drug-like diverse molecules for synthesizability evaluation; those with excessively high synthetic difficulty are removed, ultimately forming a large-scale molecular glue virtual library with structural diversity, synthesizability and drug-likeness.

Compounds in the library can be synthesized in only 1-2 chemical reaction steps. With MCE’s experienced chemical synthesis team, custom synthesis of different scales from milligram to kilogram can be easily achieved to meet diverse customer needs.

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).

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.