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.


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 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 utilise our cutting-edge, exclusive workflow to develop focused 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
Q8TBZ6

UPID:
TM10A_HUMAN

ALTERNATIVE NAMES:
RNA (guanine-9-)-methyltransferase domain-containing protein 2; tRNA (guanine(9)-N(1))-methyltransferase TRMT10A

ALTERNATIVE UPACC:
Q8TBZ6; B2R8X7; Q9Y2T9

BACKGROUND:
The enzyme tRNA methyltransferase 10 homolog A, also referred to as tRNA (guanine(9)-N(1))-methyltransferase TRMT10A, plays a critical role in the post-transcriptional modification of tRNAs by methylation. This process is essential for the proper functioning of tRNAs and, by extension, the overall protein synthesis machinery within cells.

THERAPEUTIC SIGNIFICANCE:
Given its association with the autosomal recessive disease characterized by microcephaly, intellectual disability, and metabolic challenges, TRMT10A presents a significant target for therapeutic intervention. Exploring the enzyme's function could unveil new pathways for treating related metabolic and developmental disorders.

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