Focused On-demand Library for Septin-9

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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. Our collaborator, Reaxense, aids in their synthesis and provision.

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.

We utilise our cutting-edge, exclusive workflow to develop focused libraries.

 Fig. 1. The sreening 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.

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.







Alternative names:

MLL septin-like fusion protein MSF-A; Ovarian/Breast septin; Septin D1

Alternative UPACC:

Q9UHD8; A8K2V3; B3KPM0; B4DTL9; B4E0N2; B4E274; B7Z654; Q96QF3; Q96QF4; Q96QF5; Q9HA04; Q9UG40; Q9Y5W4


Septin-9, known by alternative names such as MLL septin-like fusion protein MSF-A and Ovarian/Breast septin, is a filament-forming cytoskeletal GTPase. It is implicated in crucial cellular processes, including cytokinesis, potentially, and the internalization of intracellular pathogens like Listeria monocytogenes and Shigella flexneri. Its role in cell division and pathogen response highlights its significance in maintaining cellular integrity.

Therapeutic significance:

Septin-9 is directly associated with Hereditary neuralgic amyotrophy (HNA), a condition characterized by recurrent episodes of severe pain and muscle weakness. Understanding the role of Septin-9 in HNA could pave the way for innovative therapeutic strategies targeting the underlying genetic variants to alleviate or prevent the debilitating symptoms of this disease.

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