AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Matrix metalloproteinase-9

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.

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 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 use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.

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

P14780

UPID:

MMP9_HUMAN

Alternative names:

92 kDa gelatinase; 92 kDa type IV collagenase; Gelatinase B

Alternative UPACC:

P14780; B2R7V9; Q3LR70; Q8N725; Q9H4Z1; Q9UCJ9; Q9UCL1; Q9UDK2

Background:

Matrix metalloproteinase-9 (MMP-9), also known as 92 kDa gelatinase, 92 kDa type IV collagenase, or Gelatinase B, is pivotal in the degradation of the extracellular matrix. This enzyme is capable of cleaving components such as type IV and V collagen, fibronectin, and generates the secreted form of ninjurin-1. MMP-9's activity is crucial for processes like leukocyte migration and potentially in bone osteoclastic resorption.

Therapeutic significance:

MMP-9 is implicated in diseases such as Intervertebral disc disease, characterized by degeneration of lumbar spine disks, and Metaphyseal anadysplasia 2, a bone development disorder. Understanding the role of MMP-9 could open doors to potential therapeutic strategies for these conditions.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.