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 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 utilise our cutting-edge, exclusive workflow to develop focused 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
P16109

UPID:
LYAM3_HUMAN

ALTERNATIVE NAMES:
CD62 antigen-like family member P; Granule membrane protein 140; Leukocyte-endothelial cell adhesion molecule 3; Platelet activation dependent granule-external membrane protein

ALTERNATIVE UPACC:
P16109; Q5R344; Q8IVD1

BACKGROUND:
P-selectin, recognized for its alternative names such as Granule membrane protein 140 and Leukocyte-endothelial cell adhesion molecule 3, mediates crucial interactions between activated endothelial cells or platelets and leukocytes. Its ligand, sialyl-Lewis X, is key in the initial steps of inflammation.

THERAPEUTIC SIGNIFICANCE:
The association of P-selectin with ischemic stroke, a complex disease with multiple genetic and environmental risk factors, underscores its therapeutic potential. Targeting P-selectin's pathway offers a promising avenue for developing treatments that could significantly impact patient outcomes in stroke and related inflammatory conditions.

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