druggability evaluation

Antibody inhibitors are compounds with the same activity as the original therapeutic antibodies, which can be used as positive controls for drug efficacy evaluation and other studies. Antibody inhibitors can also assist in verifying the functional activity of the target protein. These antibody inhibitors are active in vivo and can achieve certain physiological functions by blocking or neutralizing target proteins, such as CD20, HER2, EGFR, VEGFR, TNF-α, etc. In drug screening, antibody inhibitor-based screening can be carried out to identify active compounds targeting target proteins and target diseases.

MCE can provide 1,973 antibody inhibitors that can be used for drug development in cancer, immunity, infection and other hot research areas.

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.

Macrocyclic scaffolds are increasingly valued in modern drug discovery for their exceptional activity against undruggable targets (proteases, kinases, PPIs). 2026 marks a key commercial breakthrough for oral macrocyclic peptides: enlicitide, the world’s first oral PCSK9 macrocyclic peptide, has received FDA approval. Macrocyclic candidates targeting KRAS and other classic undruggable targets have also entered clinical development, validating macrocyclization as an effective strategy to overcome druggability barriers.

Two core R&D directions lead current macrocyclic drug design: AI-driven de novo generation and structural optimization of small-molecule macrocycles, and macrocyclic peptides based on sequence design and conformational engineering. Macrocycle druggability hinges on embedded linkers, which determine cyclization efficiency, final conformation and drug-like properties. Bifunctional reaction orthogonality is the core linker selection criterion. Our linker library enables stepwise intramolecular cyclization with suppressed side reactions, accommodates varied ring sizes, and covers three key reaction systems: amide condensation, nucleophilic substitution and CuAAC click chemistry.

Built on classical macrocyclization systems, the library is processed through reaction classification, bifunctional orthogonality evaluation, novelty clustering and redundancy removal, with PROTAC long-chain and ADC cleavable linkers explicitly excluded. Featuring rigid, semi-rigid and flexible scaffolds, it is widely applicable to small-molecule macrocycle synthesis and linear peptide cyclization.

A diverse compound library with favorable ADMET properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is crucial in drug discovery. Early evaluation of ADMET properties allows for the exclusion of molecules with unfavorable profiles at the initial stages, thereby reducing the risk of late-stage development failures, lowering R&D costs, and accelerating optimization of lead compounds. Based on predictions from ADMET-related AI algorithms, the compounds in this library are predicted to exhibit favorable oral bioavailability (F > 30%), reasonable plasma protein binding (PPB < 98%), minimized CYP3A4 inhibition potential (inhibition probability < 50%, CYP3A4 is the most critical drug-metabolizing enzyme in the cytochrome P450 family) , low toxicity profiles, with 140 potentially toxic substructures pre-identified and excluded via substructure searching to eliminate compounds containing hazardous fragments. The diversity library enables broad applicability in high-throughput screening (HTS) and high-content screening (HCS).

Animal disease models are used in a variety of settings in basic research, such as studies on mechanisms of disease progression and evaluation new drugs. Animal models can be broadly classified into five categories: 1) experimental, 2) spontaneous, 3) negative, 4) orphan, 5) genetically engineered. Experimental models, which are induced artificially in the laboratory, are most common. Some small molecular compounds are usually used as inducers for animal models, such as Ceruletide for inflammatory model, Azoxymethane for tumor model. These inducers are useful tool in building animal models.

MCE offers a unique collection of 52 animal model inducers, involving inflammatory model, tumor model, nervous disease model, etc. MCE Animal Disease Model library is a powerful tool for the establishment of animal disease models.