Focused On-demand Library for Inactive rhomboid protein 2

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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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 utilise our cutting-edge, exclusive workflow to develop 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.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.







Alternative names:

Rhomboid 5 homolog 2; Rhomboid family member 2; Rhomboid veinlet-like protein 5; Rhomboid veinlet-like protein 6

Alternative UPACC:

Q6PJF5; A6NEM3; A8K801; Q5U607; Q5YGQ8; Q9H6E9


Inactive rhomboid protein 2, also known as Rhomboid 5 homolog 2, plays a crucial role in regulating ADAM17 protease. This regulation is pivotal for the modulation of the epidermal growth factor (EGF) receptor ligands and TNF, impacting sleep, cell survival, proliferation, migration, and inflammation. Despite its name, it does not exhibit protease activity independently.

Therapeutic significance:

The protein's involvement in Tylosis with esophageal cancer, a condition marked by palmoplantar keratoderma, oral leukokeratosis, and a heightened risk of esophageal cancer, underscores its potential as a therapeutic target. Understanding the role of Inactive rhomboid protein 2 could open doors to novel therapeutic strategies for this syndrome.

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