AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Cystic fibrosis transmembrane conductance regulator

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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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.

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

 Fig. 1. The sreening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

P13569

UPID:

CFTR_HUMAN

Alternative names:

ATP-binding cassette sub-family C member 7; Channel conductance-controlling ATPase; cAMP-dependent chloride channel

Alternative UPACC:

P13569; Q20BG8; Q20BH2; Q2I0A1; Q2I102

Background:

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is pivotal in epithelial ion and water transport, crucial for maintaining fluid homeostasis. Known alternatively as ATP-binding cassette sub-family C member 7, this protein facilitates chloride ion transport across cell membranes, influencing airway fluid balance and pathogen defense.

Therapeutic significance:

CFTR's malfunction is central to Cystic Fibrosis, a prevalent genetic disorder affecting exocrine glands, leading to severe respiratory and digestive issues. Insights into CFTR's function and regulation offer avenues for correcting the defective chloride channel activity, presenting potential therapeutic strategies for Cystic Fibrosis and related conditions.

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