AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Archaemetzincin-1

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.

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.

Our high-tech, dedicated method is applied to construct targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

Q400G9

UPID:

AMZ1_HUMAN

Alternative names:

Archeobacterial metalloproteinase-like protein 1

Alternative UPACC:

Q400G9; B3KRS0; Q8TF51

Background:

Archaemetzincin-1, also known as Archeobacterial metalloproteinase-like protein 1, is identified as a probable zinc metalloprotease. This classification suggests its involvement in catalyzing the cleavage of peptide bonds in proteins, a critical process in various biological functions. The protein's unique structure and enzymatic capabilities position it as a significant subject for in-depth biochemical research.

Therapeutic significance:

Understanding the role of Archaemetzincin-1 could open doors to potential therapeutic strategies. Its enzymatic function as a metalloprotease indicates its potential involvement in key biological processes, which, if modulated, could lead to novel treatments for diseases where protease activity is dysregulated.

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