Focused On-demand Libraries - Receptor.AI 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.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by 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.


We use our state-of-the-art dedicated workflow for designing focused libraries.


 

Fig. 1. The screening 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.


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
P55268

UPID:
LAMB2_HUMAN

ALTERNATIVE NAMES:
Laminin B1s chain; Laminin-11 subunit beta; Laminin-14 subunit beta; Laminin-15 subunit beta; Laminin-3 subunit beta; Laminin-4 subunit beta; Laminin-7 subunit beta; Laminin-9 subunit beta; S-laminin subunit beta

ALTERNATIVE UPACC:
P55268; Q16321

BACKGROUND:
The protein Laminin subunit beta-2, with its various forms including Laminin-14 subunit beta and Laminin-9 subunit beta, is integral to tissue formation and cellular organization in the body. Its interaction with laminin receptors and other matrix components is essential for embryonic development.

THERAPEUTIC SIGNIFICANCE:
Involvement of Laminin subunit beta-2 in diseases such as Pierson syndrome and Nephrotic syndrome 5 highlights its clinical significance. Targeting the pathways involving Laminin subunit beta-2 offers a promising avenue for developing treatments for these genetic disorders that affect the kidneys and eyes.

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