AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Endoplasmic reticulum metallopeptidase 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.

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.

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 for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.

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

Q7Z2K6

UPID:

ERMP1_HUMAN

Alternative names:

Felix-ina

Alternative UPACC:

Q7Z2K6; B2RNA4; B3KSB1; Q8N5T5; Q9H5M1

Background:

Endoplasmic reticulum metallopeptidase 1, also known by its alternative name Felix-ina, plays a crucial role within the ovary. It is essential for the organization of somatic cells and oocytes into discrete follicular structures. This protein's unique function highlights its importance in reproductive biology and ovarian physiology.

Therapeutic significance:

Understanding the role of Endoplasmic reticulum metallopeptidase 1 could open doors to potential therapeutic strategies. Its pivotal role in the formation of follicular structures in the ovary suggests that it may be a key target for addressing reproductive health issues.

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