AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Membrane metallo-endopeptidase-like 1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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 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 sreening 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.

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

Q495T6

UPID:

MMEL1_HUMAN

Alternative names:

Membrane metallo-endopeptidase-like 2; NEP2(m); Neprilysin II; Neprilysin-2

Alternative UPACC:

Q495T6; B9DI79; Q495T7; Q495T8; Q5SZS6; Q96PH9

Background:

Membrane metallo-endopeptidase-like 1, also known as Neprilysin-2, plays a crucial role in sperm function and early embryonic development. It degrades a variety of small peptides, preferring those under 3 kDa with neutral bulky aliphatic or aromatic residues. Its substrate specificity mirrors that of MME, targeting the same amide bonds.

Therapeutic significance:

Understanding the role of Membrane metallo-endopeptidase-like 1 could open doors to potential therapeutic strategies. Its involvement in key reproductive processes highlights its significance in fertility treatments and embryonic health.

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