AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Laminin subunit gamma-3

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.

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 features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

Our top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.

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

Q9Y6N6

UPID:

LAMC3_HUMAN

Alternative names:

Laminin-12 subunit gamma; Laminin-14 subunit gamma; Laminin-15 subunit gamma

Alternative UPACC:

Q9Y6N6; B1APX9; B1APY0; Q59H72

Background:

Laminin subunit gamma-3, also known as Laminin-12 subunit gamma, Laminin-14 subunit gamma, and Laminin-15 subunit gamma, plays a pivotal role in the architecture of basement membranes. It is instrumental in cell attachment, migration, and tissue organization during embryonic development by interacting with other extracellular matrix components.

Therapeutic significance:

The protein's involvement in cortical malformations occipital, a condition characterized by seizures and specific brain malformations, underscores its potential as a target for therapeutic intervention. Understanding the role of Laminin subunit gamma-3 could open doors to potential therapeutic strategies.

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