low molecular weight

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 2,253 high-structurally diverse 'RO3' compliant fragment molecules from large-scale fragment molecules, which can be applied to fragment based drug development.

With the aging population and increasing competitive pressures, neurodegenerative diseases of the central nervous system (CNS) have become a serious medical challenge in modern society, including Parkinson's disease, Alzheimer's disease, brain tumors, and multiple sclerosis. However, the success rate of CNS drug development remains remarkably low, primarily due to the blood-brain barrier (BBB). The blood-brain barrier (BBB) is a semipermeable barrier structure that surrounds the microvasculature of the CNS. In capillaries, the wedged endothelial cells are tightly packed and wedge-shaped, lining the interior of the vessels to form extensive tight junctions. Along with a range of receptors, transporters, efflux pumps, and other cellular components, this barrier regulates the entry and exit of molecules between the bloodstream and the brain. The intact BBB blocks the passage of most blood-borne substances into the brain, preventing nearly 100% of large-molecule drugs and over 98% of small-molecule drugs from entering. Compared to non-CNS drugs, physicochemical properties such as hydrogen bonds, lipophilicity, and molecular weight significantly influence a compound's ability to cross the BBB. Using artificial intelligence (AI) algorithms to predict BBB permeability, a predicted value greater than 0.75 indicates that the compound has strong potential to cross the BBB, providing a promising starting point for CNS drug discovery.

In contrast to the high conservation of conventional orthosteric sites, allosteric sites possess structural characteristics of low conservation, high hydrophobicity, weak polarity, confined spatial geometry, and dynamic cryptic properties. There is a significant difference between their core structures and orthosteric pockets — allosteric pockets are mostly dynamic grooves formed by protein conformational changes, subunit interface clefts, or shallow depressions, rather than the rigid "keyhole" structure of orthosteric sites. With looser spatial constraints, allosteric sites have the advantages of high selectivity and low off-target risk, and have become an important direction in new drug discovery.

Based on the dynamic, hydrophobic, and narrow-long spatial characteristics of allosteric pockets, MCE has performed targeted modification and screening of fragments. The screening criteria strictly conform to the requirements of allosteric binding: molecular weight is controlled at 120–280 Da (to meet the core needs of small molecules in fragment libraries and high derivatization), hydrogen bond donors (HBD ≤ 2), hydrogen bond acceptors (HBA ≤ 3), polar surface area (PSA = 30–80 Ų), rotatable bonds (≤ 2), moderate hydrophobicity (cLogP = 1–3.5), no strongly ionizable groups, and both appropriate rigidity and conformational flexibility to adapt to the dynamic changes of the pocket. Meanwhile, combined with the results of principal moment of inertia (PMI) analysis, fragments with high 3D diversity were obtained. Such fragments have good shape complementarity with allosteric pockets, ensuring that the fragments can smoothly enter the allosteric pockets and form stable binding, while providing room for subsequent optimization and derivation.

This library contains 1,800 structurally diverse fragment molecules with excellent drug-like properties, suitable for allosteric drug development and the design and optimization of allosteric sites. It combines the

Owing to the high conservation of orthosteric sites, conventional orthosteric drugs frequently suffer from poor subtype selectivity, off-target toxicity, and drug resistance, severely restricting their clinical application. In contrast, allosteric sites feature low conservation, high hydrophobicity, weak polarity, confined spatial geometry, and dynamic cryptic properties. These characteristics endow allosteric modulators with distinct advantages including high selectivity, functional tunability, and improved safety, making allosteric therapy a key direction in modern drug discovery.

MCE has curated nearly 1,000 structurally disclosed clinical-stage allosteric modulators. By analyzing allosteric protein–ligand complex structures from the PDB database, we extracted core pharmacophores and privileged scaffolds. Adopting a rational design strategy of “scaffold derivation + allosteric physicochemical filtering”, we performed secondary screening on the derived compounds strictly following the optimal physicochemical principles for allosteric binding based on universal allosteric pocket properties: molecular weight 300–500 Da, HBD ≤ 3, HBA = 3–8, PSA = 70–120 Ų, rotatable bonds ≤ 6, highly rigid scaffolds, cLogP = 1.0–3.8, and no strongly ionizable groups. The selected compounds exhibit high rigidity and shape complementarity, making them well-suited for targeting shallow, dynamic, and hydrophobic-dominated allosteric pockets.

This allosteric modulator library contains 4,315 structurally diverse, lead-like compounds dedicated to allosteric drug development, allosteric site targeting, and allosteric modulator screening. It is suitable for kinases, GPCRs, and other important drug targets. All compounds are analogs of clinical-stage allosteric modulators with a similarity score > 0.6, combining excellent druggability and allosteric binding potential. It provides a highly efficient tool for early-stage allosteric drug discovery.