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.


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 effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


Our high-tech, dedicated method is applied to construct targeted libraries.


 

Fig. 1. The screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast 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
Receptor.AI
 
UPACC
Q8N126

UPID:
CADM3_HUMAN

ALTERNATIVE NAMES:
Brain immunoglobulin receptor; Immunoglobulin superfamily member 4B; Nectin-like protein 1; Synaptic cell adhesion molecule 3; TSLC1-like protein 1

ALTERNATIVE UPACC:
Q8N126; Q8IZQ9; Q9NVJ5; Q9UJP1

BACKGROUND:
The protein Cell adhesion molecule 3, known under several aliases including Brain immunoglobulin receptor and Immunoglobulin superfamily member 4B, is integral to cell-cell adhesion. It engages in calcium-independent adhesion with both similar and different molecules, including IGSF4 and NECTIN1, and may influence the architecture or functionality of cellular junctions through its interaction with EPB41L1.

THERAPEUTIC SIGNIFICANCE:
As a gene implicated in Charcot-Marie-Tooth disease, axonal, 2FF, which manifests as progressive limb weakness and atrophy, the study of Cell adhesion molecule 3 offers a promising avenue for therapeutic intervention. Delving into its function could unlock new pathways for treating this debilitating condition.

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