structural analysis

“BioDesign” approach incorporates key structural features of known pharmacologically relevant natural products (e.g. alkaloids and other secondary metabolites) into synthetically feasible medicinal chemistry scaffolds. In order to identify the privileged pharmacophores, ring systems and linkers, we have carried out statistical analysis of structural features of natural products, marketed drugs, and drug candidates.

Saturated, fused ring, spiro, and bridged systems with a tendency towards multiple chiral centers are highly privileged among natural products and marketed drugs yet these structures are very poorly represented in commercial libraries. This library addressed this market need by incorporating these privileged elements into the design of novel synthetic molecules with high molecular framework diversity, multiple stereogenic centers (≥2), and degree of saturation (Fsp3 > 0.5).

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.

Peptides are a group of biologically active substances that are involved in various cellular functions of organisms. Peptides are often used in functional analysis, vaccine research and especially in the field of drug research and development. At present, more than 80 peptide drugs have reached the market for a wide range of diseases, including diabetes, cancer, osteoporosis, multiple sclerosis, HIV infection and chronic pain.

MedChemExpress (MCE) offers a comprehensive collection of 2,647 peptides, including bioactive peptides, amino acid derivatives, and blocking peptides. MCE Peptide Library can be used for peptide library screening, peptide drug discovery, vaccine development, target verification, structural activity research, etc.

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 2,256 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.

Unlike highly conserved orthosteric sites, allosteric sites exhibit low conservation, high hydrophobicity, weak polarity, confined geometry, and dynamic cryptic properties. Rather than rigid keyhole-like cavities, they typically appear as flexible grooves, subunit interface clefts, or shallow depressions formed by protein conformational changes.

Based on the dynamic, hydrophobic, and elongated nature of allosteric pockets, MCE has carried out targeted fragment modification and screening under strict physicochemical criteria: MW 120–280 Da, HBD ≤ 2, HBA ≤ 3, PSA 30–80 Ų, rotatable bonds ≤ 2, cLogP 1–3.5. High 3D diversity was further ensured by PMI analysis, yielding fragments with excellent shape complementarity to allosteric pockets.

This library contains 1,800 structurally diverse, drug-like fragments, this library supports allosteric drug development and pocket optimization. It significantly improves screening hit rates and enables efficient, precise early-stage R&D of allosteric drugs.

MCE 18 stands for Medicinal Chemistry Evolution 2018. This metric was established based on structural data of 28,161 patented lead molecules, 1,370 marketed innovative drugs, and nearly 30,000 investigational candidates from preclinical to Phase III stages across 23 major global pharmaceutical companies from 1950 to 2018. After scaffold clustering analysis, a scoring model was constructed by integrating five three dimensional scaffold characteristics, including aromatic rings (AR), non aromatic heterocycles (NAR), chiral centers (CHIRAL), spirocycles (SPIRO), and the sp³ carbon ratio in cyclic and acyclic moieties, enabling quantitative assessment of molecular scaffold novelty and three dimensional complexity.

According to the score distribution of patented molecules, the top 25% of the original patent dataset was defined as the high novelty region. MCE 18 high scoring compounds selected based on this criterion can effectively avoid scaffold patent conflicts and intellectual property risks from the source. Molecules in this range typically feature a high sp³ carbon ratio, abundant chiral centers, spirocycles, and fused heterocycles with prominent three dimensional conformations. Their spatial properties allow precise matching to complex non traditional undruggable target pockets such as PPI interfaces and allosteric sites, making them ideal structural types for early stage screening of First in class drugs.

MCE‑18 Novelty Focused drug‑Like library strictly selects molecules from the aforementioned high scoring range, containing more than 10,000 premium drug like molecules with highly diverse scaffolds and rich 3D diversity. It can be used for high throughput screening of well established targets such as kinases, GPCRs, and proteases, and is especially suitable for hit identification in allosteric modulation, protein–protein interactions, and various undruggable orphan targets, fully supporting early stage drug discovery for cutting edge innovat

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 6,077 mass spectrometry human metabolites that can be used for metabolite identification and quantification, functional cell detection and phenotypic screening of mass spectrometry.

Natural products are small-molecule compounds produced in nature, derived from animals, plants, and microorganisms, including both primary and secondary metabolites. With their structural diversity and favorable biological activities, natural products have long been an important source for drug discovery. Traditional natural product research has often focused on isolating single active components, whereas metabolomics emphasizes a holistic approach—comprehensively detecting all metabolites in a sample and systematically capturing both known and unknown constituents. Consequently, mass spectrometry‑based metabolomics databases have become a key technological support for screening known components and identifying unknown compounds from natural sources.

MCE Mass Spectrometry Natural Product Library contains 4,412 natural products, covering multiple structural classes, including sugars and glycosides, phenylpropanoids, quinones, flavonoids, terpenoids, etc. All compounds have undergone rigorous quality control by LC/MS and other analytical methods, and can serve as high‑purity reference standards for metabolite identification.

Metabolomics is the large-scale study of cellular metabolic complement, with proven utility in both basic and applied studies of plants, microorganisms, and mammals. As an important tool for the study of complex biological systems, metabolomics monitors the complex molecular networks that exist in the natural flow of information from genes to mRNA and proteins to organisms. The metabolome is composed of biomolecules that most closely resemble the phenotype of an organism, and changes in its composition can easily lead to the production of diseases. Therefore, metabolomics has received much attention in drug target discovery, drug response and translational research of disease mechanisms. Mass spectrometry-based metabolomics methods can simultaneously detect and quantify thousands of metabolite signatures, thereby characterizing the pathophysiological mechanisms of various biomedical symptoms.

MCE can provide 661 mass spectrometry human endogenous metabolites that can be used for metabolite identification and quantification, functional cell detection and phenotypic screening of mass spectrometry.