AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for cGMP-gated cation channel alpha-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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner 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.

We use our state-of-the-art dedicated workflow for designing focused libraries.

 Fig. 1. The sreening 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

Reaxense

upacc

P29973

UPID:

CNGA1_HUMAN

Alternative names:

Cyclic nucleotide-gated cation channel 1; Cyclic nucleotide-gated channel alpha-1; Cyclic nucleotide-gated channel, photoreceptor; Rod photoreceptor cGMP-gated channel subunit alpha

Alternative UPACC:

P29973; A8K7K6; J3KPZ2; Q16279; Q16485; Q4W5E3

Background:

The cGMP-gated cation channel alpha-1, known by alternative names such as Cyclic nucleotide-gated cation channel 1 and Rod photoreceptor cGMP-gated channel subunit alpha, plays a pivotal role in the phototransduction pathway. It functions as a subunit of the rod cyclic GMP-gated cation channel, crucial for converting light into electrical signals in rod photoreceptors.

Therapeutic significance:

Retinitis pigmentosa 49, a form of retinal dystrophy, is directly linked to mutations affecting this protein. Understanding the role of cGMP-gated cation channel alpha-1 could open doors to potential therapeutic strategies for this and similar visual impairments.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.