AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Max-interacting protein 1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 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 employ our advanced, specialised process to create targeted libraries for protein-protein interfaces.

 Fig. 1. The sreening workflow of Receptor.AI

This process entails comprehensive molecular simulations of the target protein, individually and in complex with essential partner proteins, along with ensemble virtual screening that focuses on conformational mobility in both its free and complex states. Potential binding pockets are considered at the protein-protein interaction interface and in remote allosteric locations to address every conceivable mechanism 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

P50539

UPID:

MXI1_HUMAN

Alternative names:

Class C basic helix-loop-helix protein 11

Alternative UPACC:

P50539; B1ANN7; D3DR25; D3DRA9; Q15887; Q6FHW2; Q96E53

Background:

Max-interacting protein 1, also known as Class C basic helix-loop-helix protein 11, plays a crucial role in cellular processes by acting as a transcriptional repressor. It forms a complex with MAX, recognizing the core sequence 5'-CAC[GA]TG-3', thereby antagonizing MYC transcriptional activity. This interaction is pivotal in regulating gene expression and maintaining cellular homeostasis.

Therapeutic significance:

Given its involvement in prostate cancer, where disease susceptibility is linked to gene variants, Max-interacting protein 1 represents a promising target for therapeutic intervention. Understanding its role in cancer biology could lead to the development of novel strategies aimed at modulating its activity, potentially offering new avenues for treatment of prostate cancer and enhancing patient outcomes.

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