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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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

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 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
Q9BWT3

UPID:
PAPOG_HUMAN

ALTERNATIVE NAMES:
Neo-poly(A) polymerase; Polynucleotide adenylyltransferase gamma; SRP RNA 3'-adenylating enzyme; Signal recognition particle RNA-adenylating enzyme

ALTERNATIVE UPACC:
Q9BWT3; B2RBH4; Q59G05; Q969N1; Q9H8L2; Q9HAD0

BACKGROUND:
The enzyme Poly(A) polymerase gamma, with alternative names such as Polynucleotide adenylyltransferase gamma, is integral to RNA biology. It adenylates the 3'-terminal of mRNA precursors, impacting signal recognition particle (SRP) RNA, nuclear 7SK RNA, and U2 small nuclear RNA. This modification is essential for RNA maturation and functionality.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Poly(A) polymerase gamma offers a pathway to novel therapeutic avenues. Its critical role in RNA maturation and stability underlines its potential as a target in developing treatments for diseases linked to RNA processing errors.

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