AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Probable ATP-dependent RNA helicase DDX53

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 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 utilise our cutting-edge, exclusive workflow to develop 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

Q86TM3

UPID:

DDX53_HUMAN

Alternative names:

Cancer-associated gene protein; Cancer/testis antigen 26; DEAD box protein 53; DEAD box protein CAGE

Alternative UPACC:

Q86TM3; Q0D2N2; Q6NVV4

Background:

The Probable ATP-dependent RNA helicase DDX53, also known by its alternative names such as Cancer-associated gene protein, Cancer/testis antigen 26, and DEAD box protein 53, plays a crucial role in RNA processing mechanisms. This protein belongs to the DEAD box protein family, characterized by their conserved motif Asp-Glu-Ala-Asp (DEAD), which is essential for their helicase activity.

Therapeutic significance:

Understanding the role of Probable ATP-dependent RNA helicase DDX53 could open doors to potential therapeutic strategies. Its involvement in RNA processing suggests its potential impact on gene expression regulation, which is a critical area in cancer research and therapy development.

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