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.


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.


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.


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
Q9NX46

UPID:
ADPRS_HUMAN

ALTERNATIVE NAMES:
ADP-ribose glycohydrolase ARH3; ADP-ribosylhydrolase 3; O-acetyl-ADP-ribose deacetylase ARH3; Poly(ADP-ribose) glycohydrolase ARH3; [Protein ADP-ribosylarginine] hydrolase-like protein 2; [Protein ADP-ribosylserine] hydrolase

ALTERNATIVE UPACC:
Q9NX46; Q53G94; Q6IAB8; Q9BY47

BACKGROUND:
The enzyme ADP-ribosylhydrolase ARH3 plays a pivotal role in the cellular response to DNA damage by removing mono-ADP-ribose from serine residues and breaking down free poly(ADP-ribose), thereby preventing cell death. Its function underscores the intricate mechanisms of DNA repair and cell survival.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of ADP-ribosylhydrolase ARH3 could open doors to potential therapeutic strategies, especially in the context of its critical function in neurodegenerative diseases characterized by stress-induced seizures and developmental regression.

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