Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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
P10114

UPID:
RAP2A_HUMAN

ALTERNATIVE NAMES:
RbBP-30

ALTERNATIVE UPACC:
P10114; B2RCJ1; Q5JSC1; Q5JSC2

BACKGROUND:
The Ras-related protein Rap-2a, known alternatively as RbBP-30, functions as a small GTP-binding protein. It transitions between inactive GDP-bound and active GTP-bound forms, regulating effectors including MAP4K4, MINK1, and TNIK. It is part of a signaling complex with NEDD4, RAP2A, and TNIK, essential for the regulation of neuronal dendrite growth and branching. Beyond its neuronal role, Rap-2a is involved in various signaling pathways that govern cytoskeletal changes, cell movement, adhesion, and spreading.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Ras-related protein Rap-2a holds promise for unveiling new therapeutic avenues.

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