Focused On-demand Library for Hyaluronidase-1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

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.

We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.







Alternative names:

Hyaluronoglucosaminidase-1; Lung carcinoma protein 1

Alternative UPACC:

Q12794; Q6FH23; Q6PIZ6; Q7KYU2; Q7LE34; Q8NFK5; Q8NFK6; Q8NFK7; Q8NFK8; Q8NFK9; Q93013; Q9UKD5; Q9UNI8


Hyaluronidase-1, also known as Hyaluronoglucosaminidase-1 or Lung carcinoma protein 1, is encoded by the gene with the accession number Q12794. This protein plays a pivotal role in the degradation of glycosaminoglycans, specifically hyaluronan, which is crucial for maintaining extracellular matrix integrity. Its involvement in promoting tumor progression and potentially inhibiting TGFB1-enhanced cell growth highlights its multifaceted role in cellular processes.

Therapeutic significance:

The protein's association with Mucopolysaccharidosis 9, a lysosomal storage disease characterized by excessive accumulation of glycosaminoglycans, underscores its therapeutic significance. Understanding the role of Hyaluronidase-1 could open doors to potential therapeutic strategies for treating this condition, which currently lacks targeted treatments. Its role in tumor progression further suggests its potential as a target in cancer therapy.

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