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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by 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 screening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q13753

UPID:
LAMC2_HUMAN

ALTERNATIVE NAMES:
Cell-scattering factor 140 kDa subunit; Epiligrin subunit gamma; Kalinin subunit gamma; Kalinin/nicein/epiligrin 100 kDa subunit; Ladsin 140 kDa subunit; Laminin B2t chain; Laminin-5 subunit gamma; Large adhesive scatter factor 140 kDa subunit; Nicein subunit gamma

ALTERNATIVE UPACC:
Q13753; Q02536; Q02537; Q13752; Q14941; Q14DF7; Q2M1N2; Q5VYE8

BACKGROUND:
Laminin subunit gamma-2, also referred to as Kalinin subunit gamma and Laminin-5 subunit gamma, is integral to the structural integrity and function of the basement membrane. Its cell-scattering activity impacts a variety of cell types, including epithelial and endothelial cells.

THERAPEUTIC SIGNIFICANCE:
This protein's malfunction is linked to severe forms of Epidermolysis bullosa, where it contributes to blister formation and mucosal fragility. Targeting Laminin subunit gamma-2's pathway offers a promising avenue for developing treatments for these life-altering conditions.

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