Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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 features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We employ our advanced, specialised process to create targeted libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q12794

UPID:
HYAL1_HUMAN

ALTERNATIVE NAMES:
Hyaluronoglucosaminidase-1; Lung carcinoma protein 1

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

BACKGROUND:
Hyaluronidase-1, identified by its alternative names Hyaluronoglucosaminidase-1 and Lung carcinoma protein 1, plays a critical role in the cellular matrix's dynamics through the degradation of hyaluronan. Its gene, represented by the accession number Q12794, is implicated in various biological processes, including tumor progression and modulation of cell growth in response to TGFB1.

THERAPEUTIC SIGNIFICANCE:
Given its crucial role in Mucopolysaccharidosis 9, a disease marked by abnormal glycosaminoglycan accumulation, Hyaluronidase-1 emerges as a key target for therapeutic intervention. The exploration of its functions and mechanisms offers promising avenues for developing novel treatments for this disease and potentially for cancer, leveraging its involvement in tumor biology.

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