Focused On-demand Library for 72 kDa type IV collagenase

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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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 employ our advanced, specialised process to create targeted 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.







Alternative names:

72 kDa gelatinase; Gelatinase A; Matrix metalloproteinase-2; TBE-1

Alternative UPACC:

P08253; B2R6U1; B4DWH3; E9PE45; Q9UCJ8


Matrix metalloproteinase-2 (MMP2), also known as 72 kDa type IV collagenase, Gelatinase A, or TBE-1, plays a pivotal role in the remodeling of the vasculature, angiogenesis, tissue repair, and inflammation. It degrades extracellular matrix proteins and acts on nonmatrix proteins, promoting vasoconstriction and influencing myocardial cell death pathways. MMP2's involvement in the formation of fibrovascular tissues and its anti-angiogenic properties highlight its multifunctional nature.

Therapeutic significance:

MMP2's link to Multicentric osteolysis, nodulosis, and arthropathy, a syndrome characterized by severe osteolysis and additional systemic features, underscores its therapeutic potential. Understanding MMP2's role could pave the way for innovative treatments targeting bone disorders and vascular diseases, offering hope for patients suffering from these debilitating conditions.

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