Artificial intelligence (AI)

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.

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.