Focused On-demand Library for ERO1-like protein alpha

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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated 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.

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

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

Endoplasmic oxidoreductin-1-like protein; Endoplasmic reticulum oxidoreductase alpha; Oxidoreductin-1-L-alpha

Alternative UPACC:

Q96HE7; A8K9X4; A8MYW1; Q7LD45; Q9P1Q9; Q9UKV6


ERO1-like protein alpha, also known as Endoplasmic oxidoreductin-1-like protein, plays a crucial role in the formation of disulfide bonds within the endoplasmic reticulum. It reoxidizes P4HB/PDI, enabling sustained rounds of disulfide formation, and transfers electrons to oxygen, producing reactive oxygen species. This protein is essential for the proper folding of immunoglobulins and is implicated in ER stress-induced apoptosis and the retrotranslocation of cholera toxin.

Therapeutic significance:

Understanding the role of ERO1-like protein alpha could open doors to potential therapeutic strategies. Its involvement in immunoglobulin folding and stress-induced apoptosis highlights its potential as a target in managing diseases related to immune system dysfunction and cellular stress responses.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.