Focused On-demand Library for ATP-dependent RNA helicase DDX42

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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 our partner Reaxense.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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

 Fig. 1. The sreening 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 distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

DEAD box protein 42; RNA helicase-like protein; RNA helicase-related protein; SF3b DEAD box protein; Splicing factor 3B-associated 125 kDa protein

Alternative UPACC:

Q86XP3; A6NML1; A8KA43; O75619; Q68G51; Q96BK1; Q96HR7; Q9Y3V8


ATP-dependent RNA helicase DDX42, also known as DEAD box protein 42, plays a crucial role in RNA metabolism. It unwinds partially double-stranded RNAs, facilitating various RNA processes. This protein's activity is modulated by ATP and ADP, with ATP promoting RNA strand separation and ADP encouraging the annealing of complementary strands. DDX42's interaction with TP53BP2 enhances cell survival by mitigating TP53BP2's apoptosis-inducing effects.

Therapeutic significance:

Understanding the role of ATP-dependent RNA helicase DDX42 could open doors to potential therapeutic strategies. Its involvement in RNA processing and cell survival mechanisms positions it as a key target for research aimed at uncovering novel treatments for diseases where RNA metabolism and apoptosis regulation are disrupted.

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