Focused On-demand Library for LisH domain-containing protein ARMC9

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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. Our partner Reaxense helps in synthesizing and delivering these compounds.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

Our top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.







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


LisH domain-containing protein ARMC9, also known as Armadillo repeat-containing protein 9 and NS21, plays a crucial role in ciliogenesis. It is essential for the acetylation and polyglutamylation of ciliary microtubules, regulating cilium length. ARMC9 acts as a positive regulator of hedgehog signaling and may influence the trafficking and retention of GLI2 and GLI3 proteins at the ciliary tip.

Therapeutic significance:

ARMC9's involvement in Joubert syndrome 30, characterized by cerebellar ataxia, oculomotor apraxia, and other symptoms, highlights its potential as a target for therapeutic intervention. Understanding the role of ARMC9 could open doors to potential therapeutic strategies.

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