Focused On-demand Library for Protein arginine N-methyltransferase 3

Details

Focused On-demand Libraries - Receptor.AI 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 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.


Our top-notch dedicated system is used to design specialised libraries for enzymes.


 

Fig. 1. The screening 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.

PARTNER
Receptor.AI
 
UPACC
O60678

UPID:
ANM3_HUMAN

ALTERNATIVE NAMES:
Heterogeneous nuclear ribonucleoprotein methyltransferase-like protein 3

ALTERNATIVE UPACC:
O60678; A0A0A0MSN7; B4DUC7

BACKGROUND:
The enzyme Protein arginine N-methyltransferase 3, alternatively named Heterogeneous nuclear ribonucleoprotein methyltransferase-like protein 3, is a key player in the post-translational modification of proteins through arginine methylation. It is classified as a type I methyltransferase, with the ability to perform monomethylation and asymmetric dimethylation on arginine residues. This enzyme is also implicated in the regulation of retinoic acid synthesis and signaling, acting by inhibiting the ALDH1A1 enzyme.

THERAPEUTIC SIGNIFICANCE:
The exploration of Protein arginine N-methyltransferase 3's function offers a promising avenue for the development of novel therapeutic approaches. Its critical role in regulating retinoic acid pathways suggests its potential utility in designing drugs aimed at modulating these pathways for disease treatment.

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