Focused On-demand Library for Histone-arginine methyltransferase METTL23

Details

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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 includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


We employ our advanced, specialised process to create targeted 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 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
Q86XA0

UPID:
MET23_HUMAN

ALTERNATIVE NAMES:
Methyltransferase-like protein 23

ALTERNATIVE UPACC:
Q86XA0; H9ZYJ0; K7EK32

BACKGROUND:
The protein Histone-arginine methyltransferase METTL23, alternatively known as Methyltransferase-like protein 23, is instrumental in activating transcription via chromatin remodeling. It achieves this by dimethylating histone H3 at 'Arg-17'. Additionally, METTL23 plays a vital role in the epigenetic reprogramming of the paternal genome in zygotes, promoting DNA demethylation.

THERAPEUTIC SIGNIFICANCE:
Given its critical function in Intellectual developmental disorder, autosomal recessive 44, Histone-arginine methyltransferase METTL23 represents a promising target for drug discovery. Understanding the role of METTL23 could open doors to potential therapeutic strategies.

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