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.


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.


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 employ our advanced, specialised process to create 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
Q7Z3E5

UPID:
ARMC9_HUMAN

ALTERNATIVE NAMES:
Armadillo repeat-containing protein 9; Melanoma/melanocyte-specific tumor antigen KU-MEL-1; NS21

ALTERNATIVE UPACC:
Q7Z3E5; A0A087X1I8; Q53TI3; Q6P162; Q7L594; Q86WG2; Q96JF9; Q9H9R8

BACKGROUND:
The protein LisH domain-containing protein ARMC9, with alternative names such as Melanoma/melanocyte-specific tumor antigen KU-MEL-1, is pivotal in ciliogenesis. It ensures proper acetylation and polyglutamylation of ciliary microtubules and controls cilium length. Furthermore, ARMC9 is a key regulator of hedgehog signaling, potentially affecting GLI2 and GLI3 protein localization at the ciliary tip.

THERAPEUTIC SIGNIFICANCE:
Given ARMC9's critical role in the pathogenesis of Joubert syndrome 30, marked by significant neurological manifestations, it represents a promising target for drug discovery. Understanding the role of ARMC9 could open doors to potential therapeutic strategies.

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