Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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.


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.


PARTNER
Receptor.AI
 
UPACC
Q8IW75

UPID:
SPA12_HUMAN

ALTERNATIVE NAMES:
OL-64; Visceral adipose tissue-derived serine protease inhibitor; Visceral adipose-specific serpin

ALTERNATIVE UPACC:
Q8IW75

BACKGROUND:
The protein Serpin A12, known by alternative names such as OL-64 and Visceral adipose-specific serpin, is instrumental in regulating insulin's effects on the body. By specifically targeting and inhibiting the protease KLK7 in white adipose tissues, it serves a pivotal role in the metabolic processes associated with insulin sensitivity.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Serpin A12 offers promising avenues for therapeutic intervention. Given its critical involvement in insulin modulation, targeting Serpin A12 could lead to innovative treatments for metabolic diseases, potentially revolutionizing our approach to managing insulin-related conditions.

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