Focused On-demand Library for Cyclic nucleotide-gated cation channel beta-1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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.

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.

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

 Fig. 1. The sreening 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.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.







Alternative names:

Cyclic nucleotide-gated cation channel 4; Cyclic nucleotide-gated cation channel gamma; Cyclic nucleotide-gated cation channel modulatory subunit; Cyclic nucleotide-gated channel beta-1; Glutamic acid-rich protein

Alternative UPACC:

Q14028; H3BN09; O43636; Q13059; Q14029; Q9UMG2


Cyclic nucleotide-gated cation channel beta-1, also known as CNG channel beta-1, plays a pivotal role in visual and olfactory signal transduction. It forms a crucial part of cyclic nucleotide-gated (CNG) channels, facilitating the regulation of ion flow into rod photoreceptor outer segments in response to changes in intracellular cGMP levels. This protein's functionality is enhanced by its isoform GARP2, which modulates rod photoreceptor phosphodiesterase activity, crucial for minimizing 'dark noise' and enabling photon detection.

Therapeutic significance:

Given its critical role in visual processes, Cyclic nucleotide-gated cation channel beta-1 is directly implicated in Retinitis pigmentosa 45, a retinal dystrophy characterized by night vision blindness and progressive loss of visual field. Understanding the protein's function and its genetic variants could pave the way for innovative treatments targeting the underlying mechanisms of this and potentially other related visual disorders.

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