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.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


We utilise our cutting-edge, exclusive workflow to develop focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


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
Q12918

UPID:
KLRB1_HUMAN

ALTERNATIVE NAMES:
C-type lectin domain family 5 member B; HNKR-P1a; Natural killer cell surface protein P1A

ALTERNATIVE UPACC:
Q12918; Q24K24

BACKGROUND:
The protein Killer cell lectin-like receptor subfamily B member 1, with alternative names such as C-type lectin domain family 5 member B, HNKR-P1a, and Natural killer cell surface protein P1A, serves as a key regulator in the immune response. It inhibits the cytotoxicity of NK cells, triggers significant elevation of intracellular ceramide, and stimulates kinases AKT1/PKB and RPS6KA1/RSK1. Additionally, it promotes T-cell proliferation and acts as a lectin, binding to Gal-alpha(1,3)Gal and N-acetyllactosamine epitopes, and interacts with CLEC2D/LLT1 to inhibit NK cell-mediated cytotoxicity.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Killer cell lectin-like receptor subfamily B member 1 could open doors to potential therapeutic strategies.

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