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 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.


Our top-notch dedicated system is used to design specialised 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 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
Receptor.AI
 
UPACC
Q9NXZ2

UPID:
DDX43_HUMAN

ALTERNATIVE NAMES:
Cancer/testis antigen 13; DEAD box protein 43; DEAD box protein HAGE; Helical antigen

ALTERNATIVE UPACC:
Q9NXZ2; B4E0C8; Q6NXR1

BACKGROUND:
Probable ATP-dependent RNA helicase DDX43, recognized under names such as Cancer/testis antigen 13 and DEAD box protein HAGE, is integral to the RNA metabolic processes. As a member of the DEAD box helicase family, it is implicated in the regulation of RNA stability and translation, playing a pivotal role in cellular homeostasis and gene expression.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Probable ATP-dependent RNA helicase DDX43 holds the key to unlocking novel therapeutic avenues. Given its central role in RNA metabolism, targeting DDX43 could lead to innovative treatments for diseases where RNA processing is compromised.

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