Focused On-demand Library for Poly(A)-specific ribonuclease PARN

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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 our partner 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 use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

Deadenylating nuclease; Deadenylation nuclease; Polyadenylate-specific ribonuclease

Alternative UPACC:

O95453; B2RCB3; B4DDG8; B4DSB0; B4DWR4; B4E1H6


Poly(A)-specific ribonuclease (PARN) plays a pivotal role in mRNA turnover, targeting poly(A) tails for degradation. This enzyme's interaction with mRNA cap structures enhances its efficiency in poly(A) tail removal, a key step in controlling mRNA stability and gene expression. PARN's involvement extends to the decay of mRNAs with premature stop codons and the destabilization of mRNAs with AU-rich elements, highlighting its critical function in post-transcriptional regulation.

Therapeutic significance:

PARN's dysfunction is linked to dyskeratosis congenita and telomere-related pulmonary fibrosis, diseases characterized by compromised telomere maintenance and bone marrow failure. Understanding PARN's role could open doors to potential therapeutic strategies targeting these conditions, offering hope for interventions that could mitigate the severe manifestations and improve patient outcomes.

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