Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


Our top-notch dedicated system is used to design specialised 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 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
Receptor.AI
 
UPACC
Q13751

UPID:
LAMB3_HUMAN

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

BACKGROUND:
The protein Laminin subunit beta-3, with aliases such as Kalinin B1 chain and Nicein subunit beta, is integral to the formation and maintenance of the extracellular matrix, facilitating cell adhesion and tissue organization. Its interaction with laminin receptors underscores its pivotal role in embryonic development.

THERAPEUTIC SIGNIFICANCE:
Involvement of Laminin subunit beta-3 in diseases like junctional epidermolysis bullosa and amelogenesis imperfecta highlights its therapeutic potential. Targeting the pathways influenced by this protein could lead to innovative treatments for these genetic disorders, emphasizing the importance of research into its functions and mechanisms.

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