AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Ras-related protein Rab-23

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner Reaxense.

Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.

We use our state-of-the-art dedicated workflow for designing focused libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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

Reaxense

upacc

Q9ULC3

UPID:

RAB23_HUMAN

Alternative names:

-

Alternative UPACC:

Q9ULC3; B2R9I5; Q68DJ6; Q8NI06; Q9P023

Background:

Ras-related protein Rab-23 plays a pivotal role in intracellular membrane trafficking, influencing vesicle formation, movement, and fusion. It cycles between active and inactive states, engaging with various effectors to regulate processes such as autophagic vacuole assembly and pathogen defense. Rab-23's interaction with SUFU and GLI transcription factors underscores its significance in cellular signaling and development.

Therapeutic significance:

Carpenter syndrome 1, a disorder marked by skeletal, cardiac, and developmental anomalies, is linked to mutations in the gene encoding Rab-23. Understanding Rab-23's function could illuminate pathways for targeted therapies, offering hope for managing or correcting the multifaceted manifestations of this condition.

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