Focused On-demand Library for Inhibitor of growth 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.

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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We employ our advanced, specialised process to create targeted libraries for protein-protein interfaces.

 Fig. 1. The sreening workflow of Receptor.AI

It features thorough molecular simulations of the target protein, both isolated and in complex with key partner proteins, complemented by ensemble virtual screening that accounts for conformational mobility in the unbound and complex states. The tentative binding sites are explored on the protein-protein interaction interface and at remote allosteric locations, encompassing the entire spectrum of potential mechanisms of action.

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:


Alternative UPACC:

Q9UK53; O00532; O43658; Q53ZR3; Q5T9G8; Q5T9G9; Q5T9H0; Q5T9H1; Q9H007; Q9HD98; Q9HD99; Q9NS83; Q9P0U6; Q9UBC6; Q9UIJ1; Q9UIJ2; Q9UIJ3; Q9UIJ4; Q9UK52


Inhibitor of growth protein 1 plays a pivotal role in cell cycle regulation and apoptosis, acting as a co-regulator of p53, a well-known tumor suppressor. Its involvement in modulating p53-dependent transcriptional activation underscores its significance in cellular homeostasis.

Therapeutic significance:

Given its critical function in the negative regulatory pathway of cell growth and its implication as a tumor suppressor gene, Inhibitor of growth protein 1 is directly linked to Squamous cell carcinoma of the head and neck. This association highlights its potential as a target for therapeutic intervention in non-melanoma skin cancer.

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