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.


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 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.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.


PARTNER
Receptor.AI
 
UPACC
Q92600

UPID:
CNOT9_HUMAN

ALTERNATIVE NAMES:
Cell differentiation protein RQCD1 homolog

ALTERNATIVE UPACC:
Q92600; B2RPI0; B5MDQ4; B7Z1E5; Q96IX4

BACKGROUND:
The CCR4-NOT transcription complex subunit 9, known for its alternative name Cell differentiation protein RQCD1 homolog, is a key player in the cellular mRNA deadenylation process. As part of the CCR4-NOT complex, it contributes to bulk mRNA degradation, translational repression, and general transcription regulation. Its capacity to bind specific oligonucleotides and enhance nuclear hormone receptors' transcriptional activity, except for ESR1, underscores its significant regulatory role in gene expression.

THERAPEUTIC SIGNIFICANCE:
The exploration of CCR4-NOT transcription complex subunit 9's functions offers a promising avenue for therapeutic intervention. Given its central role in gene expression regulation and cell differentiation, targeting this protein could lead to innovative treatments for diseases where gene expression is dysregulated.

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