Focused On-demand Library for Caveolin-1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate 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.

We employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

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.







Alternative names:


Alternative UPACC:

Q03135; Q9UGP1; Q9UNG1; Q9UQH6


Caveolin-1 functions as a scaffolding protein within caveolar membranes, crucial for caveolae formation and lipid raft targeting. It interacts with G-protein alpha subunits, influencing their activity, and plays a role in T-cell activation and Wnt pathway signaling through its interactions with DPP4 and CTNNB1, respectively. Additionally, Caveolin-1 is involved in the regulation of TGFB1-mediated SMAD2/3 activation.

Therapeutic significance:

Caveolin-1 is implicated in several diseases, including Congenital generalized lipodystrophy 3, characterized by extreme insulin resistance and early onset diabetes; Pulmonary hypertension, primary, 3, with elevated pulmonary arterial pression; and Lipodystrophy, familial partial, 7, involving abnormal fat distribution. Understanding the role of Caveolin-1 could open doors to potential therapeutic strategies for these conditions.

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