AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Transmembrane emp24 domain-containing protein 1

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.

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

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of 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

Q13445

UPID:

TMED1_HUMAN

Alternative names:

Interleukin-1 receptor-like 1 ligand; Putative T1/ST2 receptor-binding protein; p24 family protein gamma-1

Alternative UPACC:

Q13445

Background:

Transmembrane emp24 domain-containing protein 1, known by its alternative names such as Interleukin-1 receptor-like 1 ligand and Putative T1/ST2 receptor-binding protein, plays a crucial role in vesicular protein trafficking, particularly in the early secretory pathway. It may act as a cargo receptor for secretory cargo molecules and be involved in vesicle coat formation. Additionally, it enhances IL-33-mediated IL-8 and IL-6 production by interacting with interleukin-33 receptor IL1RL1 and modulates innate immune signaling through the cGAS-STING pathway by interacting with RNF26.

Therapeutic significance:

Understanding the role of Transmembrane emp24 domain-containing protein 1 could open doors to potential therapeutic strategies.

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