Focused On-demand Library for tRNA (guanine(26)-N(2))-dimethyltransferase

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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate 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.

We use our state-of-the-art dedicated workflow for designing focused 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:

tRNA 2,2-dimethylguanosine-26 methyltransferase; tRNA(guanine-26,N(2)-N(2)) methyltransferase; tRNA(m(2,2)G26)dimethyltransferase

Alternative UPACC:

Q9NXH9; O76103; Q548Y5; Q8WVA6


The tRNA (guanine(26)-N(2))-dimethyltransferase, also known as tRNA 2,2-dimethylguanosine-26 methyltransferase, plays a crucial role in the post-transcriptional modification of tRNA. It specifically dimethylates a guanine residue at position 26 of most tRNAs, utilizing S-adenosyl-L-methionine as the methyl group donor. This modification is vital for the stability and function of tRNA molecules.

Therapeutic significance:

Understanding the role of tRNA (guanine(26)-N(2))-dimethyltransferase could open doors to potential therapeutic strategies. Its involvement in Intellectual developmental disorder, autosomal recessive 68, underscores the importance of its function in cognitive development and highlights its potential as a target for therapeutic intervention.

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