AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Probable ATP-dependent RNA helicase DDX23

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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 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 our associate 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 sreening 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.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.

partner

Reaxense

upacc

Q9BUQ8

UPID:

DDX23_HUMAN

Alternative names:

100 kDa U5 snRNP-specific protein; DEAD box protein 23; PRP28 homolog; U5-100kD

Alternative UPACC:

Q9BUQ8; B2R600; B4DH15; O43188

Background:

Probable ATP-dependent RNA helicase DDX23, also known as 100 kDa U5 snRNP-specific protein, DEAD box protein 23, PRP28 homolog, and U5-100kD, plays a crucial role in pre-mRNA splicing. Its phosphorylated form, activated by SRPK2, is essential for the formation of the spliceosomal B complex. Beyond spliceosome assembly, DDX23 is vital for preventing the formation of incorrect R-loops during transcription, which are detrimental DNA:RNA hybrids.

Therapeutic significance:

Understanding the role of Probable ATP-dependent RNA helicase DDX23 could open doors to potential therapeutic strategies.

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