AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for ERO1-like protein beta

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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

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 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 is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

Q86YB8

UPID:

ERO1B_HUMAN

Alternative names:

Endoplasmic reticulum oxidoreductase beta; Endoplasmic reticulum oxidoreductin-1-like protein B; Oxidoreductin-1-L-beta

Alternative UPACC:

Q86YB8; B4DF57; Q5T1H4; Q8IZ11; Q9NR62

Background:

ERO1-like protein beta, also known as Endoplasmic reticulum oxidoreductase beta, plays a crucial role in disulfide bond formation within the endoplasmic reticulum. It primarily reoxidizes P4HB/PDI, facilitating continuous rounds of disulfide bond formation. This protein also interacts with other members of the protein disulfide isomerase family, albeit at varying efficiencies, and contributes to the production of reactive oxygen species through electron transfer to molecular oxygen.

Therapeutic significance:

Understanding the role of ERO1-like protein beta could open doors to potential therapeutic strategies, particularly in diseases where disulfide bond formation is disrupted or in conditions characterized by oxidative stress.

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