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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


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


 

Fig. 1. The screening workflow of Receptor.AI

This includes comprehensive molecular simulations of the receptor in its native membrane environment, paired with ensemble virtual screening that factors in its conformational mobility. In cases involving dimeric or oligomeric receptors, the entire functional complex is modelled, pinpointing potential binding pockets on and between the subunits to capture the full range of mechanisms of action.


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
Q8NI17

UPID:
IL31R_HUMAN

ALTERNATIVE NAMES:
Cytokine receptor-like 3; GLM-R; Gp130-like monocyte receptor; ZcytoR17

ALTERNATIVE UPACC:
Q8NI17; A6NIF8; Q2TBA1; Q6EBC3; Q6EBC4; Q6EBC5; Q6EBC6; Q6UWL8; Q8WYJ0

BACKGROUND:
Interleukin-31 receptor subunit alpha, known variably as Cytokine receptor-like 3, GLM-R, and ZcytoR17, is integral to initiating immune responses, particularly in the skin. By partnering with OSMR, it activates key signaling pathways via STAT3, STAT1, and STAT5, and is implicated in the regulation of itch through IL31. Additionally, it supports the survival and growth of myeloid progenitor cells, underscoring its role in hematopoiesis.

THERAPEUTIC SIGNIFICANCE:
The association of Interleukin-31 receptor subunit alpha with primary localized cutaneous amyloidosis highlights its therapeutic potential. By elucidating its function, researchers can unlock novel therapeutic strategies, particularly for skin conditions and immune system disorders. This underscores the critical need for targeted research into this receptor's mechanisms.

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