Focused On-demand Library for ATP-dependent RNA helicase DDX3X

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better activity, selectivity, and safety.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by Reaxense.


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.


We use our state-of-the-art dedicated workflow for designing 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
O00571

UPID:
DDX3X_HUMAN

ALTERNATIVE NAMES:
CAP-Rf; DEAD box protein 3, X-chromosomal; DEAD box, X isoform; Helicase-like protein 2

ALTERNATIVE UPACC:
O00571; A8K538; B4E3E8; O15536

BACKGROUND:
The protein ATP-dependent RNA helicase DDX3X, with aliases such as Helicase-like protein 2, is integral to various cellular mechanisms, including the regulation of cell growth and lipid homeostasis. It exhibits a relaxed substrate specificity, engaging with both ribo- and deoxynucleic acids, and plays a crucial role in the translation of complex mRNAs.

THERAPEUTIC SIGNIFICANCE:
Given its critical function in Intellectual developmental disorder, X-linked, syndromic, Snijders Blok type, exploring ATP-dependent RNA helicase DDX3X's biological pathways offers a promising avenue for drug discovery. Its multifaceted role in biological systems makes it an intriguing subject for scientific inquiry and therapeutic intervention.

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