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.


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 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 is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
Q96Q11

UPID:
TRNT1_HUMAN

ALTERNATIVE NAMES:
Mitochondrial tRNA nucleotidyl transferase, CCA-adding; mt CCA-adding enzyme; mt tRNA CCA-diphosphorylase; mt tRNA CCA-pyrophosphorylase; mt tRNA adenylyltransferase

ALTERNATIVE UPACC:
Q96Q11; A8K2Z6; B7WP13; C9JKA2; Q8ND57; Q9BS97; Q9Y362

BACKGROUND:
Mitochondrial tRNA nucleotidyl transferase, CCA-adding enzyme, is integral to tRNA stability and function, marking unstable tRNAs for degradation and facilitating the repair of damaged tRNAs. It uniquely adds a CCACCA sequence to unstable tRNAs, distinguishing it from stable tRNAs that receive only the CCA end. This specificity plays a critical role in tRNA surveillance and quality control.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of CCA tRNA nucleotidyltransferase 1, mitochondrial could open doors to potential therapeutic strategies. Its involvement in genetic disorders highlights its importance in cellular function and disease, presenting opportunities for drug discovery aimed at modulating its activity.

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