Focused On-demand Library for Inhibitor of growth protein 1

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 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 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 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.

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

Q9UK53

UPID:

ING1_HUMAN

Alternative names:

Alternative UPACC:

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

Background:

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.