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.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by Reaxense.


The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We utilise our cutting-edge, exclusive workflow to develop focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.


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
Q6P2P2

UPID:
ANM9_HUMAN

ALTERNATIVE NAMES:
Protein arginine N-methyltransferase 10

ALTERNATIVE UPACC:
Q6P2P2; A8KA39; B3KU92; Q6ZR58; Q8N383; Q9BT55; Q9NT98

BACKGROUND:
The enzyme Protein arginine N-methyltransferase 9, alternatively named Protein arginine N-methyltransferase 10, is integral to the methylation process of arginine residues in proteins. It specifically mediates the symmetrical dimethylation of SF3B2 and is involved in crucial cellular processes such as the regulation of alternative splicing of pre-mRNA. This regulatory mechanism is essential for the correct expression of genes and the functioning of biological systems.

THERAPEUTIC SIGNIFICANCE:
The exploration of Protein arginine N-methyltransferase 9's function offers a promising avenue for therapeutic intervention. Given its significant role in the regulation of gene expression and its potential impact on various biological pathways, targeting this protein could lead to the development of novel therapeutic approaches.

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