Focused On-demand Library for Tumor protein p73

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 carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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.


Our high-tech, dedicated method is applied to construct targeted libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

PARTNER
Receptor.AI
 
UPACC
O15350

UPID:
P73_HUMAN

ALTERNATIVE NAMES:
p53-like transcription factor; p53-related protein

ALTERNATIVE UPACC:
O15350; B7Z7J4; B7Z8Z1; B7Z9C1; C9J521; O15351; Q17RN8; Q5TBV5; Q5TBV6; Q8NHW9; Q8TDY5; Q8TDY6; Q9NTK8

BACKGROUND:
The Tumor protein p73, identified for its similarity to p53, engages in DNA damage-induced apoptosis. Its isoforms have divergent roles; some are pro-apoptotic, enhancing cell death, while others are anti-apoptotic, inhibiting p53 functions. This protein is also essential in lung ciliated cell differentiation and activates FOXJ1 expression.

THERAPEUTIC SIGNIFICANCE:
Tumor protein p73's association with primary ciliary dyskinesia and lissencephaly, diseases marked by severe respiratory and neurological symptoms, underscores its potential as a target for innovative therapeutic interventions.

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