Focused On-demand Library for tRNA (guanine-N(7)-)-methyltransferase

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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 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 top-notch dedicated system is used to design specialised libraries for enzymes.

 Fig. 1. The sreening 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.







Alternative names:

Methyltransferase-like protein 1; mRNA (guanine-N(7)-)-methyltransferase; miRNA (guanine-N(7)-)-methyltransferase; tRNA (guanine(46)-N(7))-methyltransferase; tRNA(m7G46)-methyltransferase

Alternative UPACC:

Q9UBP6; B2RBX1; H7BXF2; Q14105; Q53FS9


Methyltransferase-like protein 1, known for its pivotal role in RNA modification, catalyzes the formation of N(7)-methylguanine across various RNA species, including tRNAs, mRNAs, and miRNAs. This modification is crucial for the stabilization of tRNA structure, regulation of mRNA translation, and miRNA processing, highlighting its multifaceted role in cellular function.

Therapeutic significance:

Understanding the role of Methyltransferase-like protein 1 could open doors to potential therapeutic strategies. Its involvement in fundamental RNA processes suggests its potential as a target in diseases where these pathways are dysregulated.

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