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.


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 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 use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance 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
Q6PHR2

UPID:
ULK3_HUMAN

ALTERNATIVE NAMES:
Unc-51-like kinase 3

ALTERNATIVE UPACC:
Q6PHR2; B2RXK3; B4DFT0; B4DRQ7; D3DW68; Q9NPN5; Q9UFS4

BACKGROUND:
The Serine/threonine-protein kinase ULK3, alternatively named Unc-51-like kinase 3, is integral to regulating Sonic hedgehog (SHH) signaling and autophagy. In the absence of SHH, it negatively regulates SHH signaling by interacting with SUFU, preventing the phosphorylation of GLI proteins. With SHH, ULK3 activates SHH signaling by autophosphorylation and phosphorylation of GLI2, enhancing its nuclear translocation. ULK3's ability to phosphorylate GLI1 and GLI3, albeit less efficiently, and its role in inducing autophagy post-cellular senescence, underscore its multifunctional nature.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Serine/threonine-protein kinase ULK3 unveils potential avenues for therapeutic intervention.

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