Focused On-demand Library for Beta-hexosaminidase subunit beta

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better activity, selectivity, and safety.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


Our high-tech, dedicated method is applied to construct targeted libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

PARTNER
Receptor.AI
 
UPACC
P07686

UPID:
HEXB_HUMAN

ALTERNATIVE NAMES:
Beta-N-acetylhexosaminidase subunit beta; Cervical cancer proto-oncogene 7 protein; N-acetyl-beta-glucosaminidase subunit beta

ALTERNATIVE UPACC:
P07686

BACKGROUND:
The Beta-hexosaminidase subunit beta enzyme, known alternatively as Cervical cancer proto-oncogene 7 protein, is integral in the breakdown of glycoconjugates, including neutral glycolipids and certain mucopolysaccharides. Its specific activity against GM2 gangliosides, facilitated by isozyme A in conjunction with GM2A, is critical for neural function. Additionally, its role in fertilization, through the prevention of polyspermy, highlights its importance in reproductive biology.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Beta-hexosaminidase subunit beta could open doors to potential therapeutic strategies. Its direct involvement in GM2-gangliosidosis 2, through genetic variants impacting its function, presents a clear pathway for developing treatments aimed at mitigating the effects of this lysosomal storage disease, offering hope for affected individuals.

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