Focused On-demand Library for Matrix metalloproteinase-9

Details

Focused On-demand Libraries - Receptor.AI 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.


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 Reaxense.


The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


We employ our advanced, specialised process to create targeted libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance activity and selectivity.


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.

PARTNER
Receptor.AI
 
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:
The enzyme Matrix metalloproteinase-9, with alternative names such as 92 kDa gelatinase and Gelatinase B, plays a critical role in the local proteolysis of the extracellular matrix and leukocyte migration. It is known for its ability to cleave key substrates like KiSS1, NINJ1, and various types of collagen, which underscores its importance in physiological and pathological processes.

THERAPEUTIC SIGNIFICANCE:
Given its involvement in Intervertebral disc disease and Metaphyseal anadysplasia 2, MMP-9 represents a significant target for drug discovery efforts. The enzyme's role in these diseases suggests that modulating its activity could lead to innovative treatments, highlighting the therapeutic potential of MMP-9.

Looking for more information on this library or underlying technology? Fill out the form below and we will be in touch with all the details you need.