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.


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 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.


Our high-tech, dedicated method is applied to construct targeted 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.


Our library is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
Q9Y2R4

UPID:
DDX52_HUMAN

ALTERNATIVE NAMES:
ATP-dependent RNA helicase ROK1-like; DEAD box protein 52

ALTERNATIVE UPACC:
Q9Y2R4; Q86YG1; Q8N213; Q9NVE0; Q9Y482

BACKGROUND:
Probable ATP-dependent RNA helicase DDX52, alternatively named ATP-dependent RNA helicase ROK1-like and DEAD box protein 52, is essential for efficient ribosome biogenesis. It may influence cell cycle progression through the regulation of TOP motif-containing mRNAs translation, such as GTPBP4.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Probable ATP-dependent RNA helicase DDX52 offers a promising avenue for developing therapeutic strategies, targeting disorders linked to aberrant ribosome biogenesis and cell cycle control.

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