Focused On-demand Libraries - Receptor.AI 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 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 employ our advanced, specialised process to create targeted 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
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:
The Probable ATP-dependent RNA helicase DDX23, with alternative names such as 100 kDa U5 snRNP-specific protein, DEAD box protein 23, PRP28 homolog, and U5-100kD, is integral to the pre-mRNA splicing process. It requires phosphorylation by SRPK2 for spliceosomal B complex formation. Apart from its role in spliceosome formation, DDX23 is crucial in suppressing incorrect R-loops that form during transcription, which consist of a DNA:RNA hybrid and the associated non-template single-stranded DNA.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Probable ATP-dependent RNA helicase DDX23 holds promise for unveiling novel therapeutic avenues.

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