Focused On-demand Library for Nuclear factor 1 B-type

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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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 use our state-of-the-art dedicated workflow for designing 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.


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
O00712

UPID:
NFIB_HUMAN

ALTERNATIVE NAMES:
CCAAT-box-binding transcription factor; Nuclear factor I/B; TGGCA-binding protein

ALTERNATIVE UPACC:
O00712; G3V1P1; H7BYE8; O00166; Q12858; Q5VW29; Q63HM5; Q6ZNF9; Q96J45

BACKGROUND:
The Nuclear factor 1 B-type, known for its alternative names such as CCAAT-box-binding transcription factor and TGGCA-binding protein, is essential for proper brain development. It activates transcription of GFAP by binding to a specific palindromic sequence, playing a key role in cellular and viral transcription and replication.

THERAPEUTIC SIGNIFICANCE:
Associated with the disease Macrocephaly, acquired, with impaired intellectual development, Nuclear factor 1 B-type's dysfunction leads to significant neurodevelopmental challenges. Exploring its function further could unveil novel therapeutic avenues for managing macrocephaly and enhancing neurodevelopmental outcomes.

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