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

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.


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 Reaxense.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


We use our state-of-the-art dedicated workflow for designing focused 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 is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

PARTNER
Receptor.AI
 
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:
Probable ATP-dependent RNA helicase DDX53, known for its involvement in RNA metabolic processes, is identified by several names including Cancer-associated gene protein and DEAD box protein CAGE. This protein is part of the DEAD box helicase family, crucial for unwinding RNA, indicating its significant role in RNA biology.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Probable ATP-dependent RNA helicase DDX53 holds promise for uncovering new therapeutic avenues. Given its critical role in RNA metabolism, targeting DDX53 could lead to innovative treatments for diseases where RNA processing is disrupted.

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