Focused On-demand Library for Laminin subunit beta-3

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced activity, selectivity, and safety.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.

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.







Alternative names:

Epiligrin subunit bata; Kalinin B1 chain; Kalinin subunit beta; Laminin B1k chain; Laminin-5 subunit beta; Nicein subunit beta

Alternative UPACC:

Q13751; D3DT88; O14947; Q14733; Q9UJK4; Q9UJL1


Laminin subunit beta-3, known by alternative names such as Epiligrin subunit beta and Laminin-5 subunit beta, plays a crucial role in cell attachment, migration, and organization into tissues during embryonic development. It achieves this by interacting with other extracellular matrix components, highlighting its significance in cellular architecture and integrity.

Therapeutic significance:

Laminin subunit beta-3 is implicated in severe and intermediate forms of junctional epidermolysis bullosa (JEB) and amelogenesis imperfecta. These conditions underscore the protein's critical role in skin and enamel integrity, making it a target for therapeutic intervention. Understanding the role of Laminin subunit beta-3 could open doors to potential therapeutic strategies for these debilitating diseases.

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