Multivariate statistical analysis

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.

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.

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.

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