Focused On-demand Library for 5-methylcytosine rRNA methyltransferase NSUN4

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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 includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

Our high-tech, dedicated method is applied to construct targeted libraries for enzymes.

 Fig. 1. The sreening 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 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:

5-methylcytosine tRNA methyltransferase NSUN4; NOL1/NOP2/Sun domain family member 4

Alternative UPACC:

Q96CB9; A8K6S6; B3KQ50; B4DHA4; Q5TDF7; Q96AN8; Q9HAJ8


The 5-methylcytosine rRNA methyltransferase NSUN4 plays a crucial role in mitochondrial ribosome assembly. It is responsible for the methylation of mitochondrial 12S rRNA, a key process in the maturation of the mitochondrial ribosome small subunit (SSU). NSUN4's activity is independent of MTERFD2/MTERF4 but is targeted to the large subunit (LSU) by these proteins, ensuring the proper assembly of SSU and LSU. Additionally, NSUN4 can methylate 16S rRNA of the LSU in vitro, a process enhanced by MTERFD/MTERF4.

Therapeutic significance:

Understanding the role of 5-methylcytosine rRNA methyltransferase NSUN4 could open doors to potential therapeutic strategies.

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