Focused On-demand Libraries - Receptor.AI 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.


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.


We utilise our cutting-edge, exclusive workflow to develop focused 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.


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
Receptor.AI
 
UPACC
Q9NZ45

UPID:
CISD1_HUMAN

ALTERNATIVE NAMES:
Cysteine transaminase CISD1; MitoNEET

ALTERNATIVE UPACC:
Q9NZ45; Q1X902

BACKGROUND:
The enzyme CDGSH iron-sulfur domain-containing protein 1, known alternatively as MitoNEET or Cysteine transaminase CISD1, is integral to amino acid and mitochondrial metabolism. It facilitates the transfer of amino groups in a reaction crucial for the synthesis of L-glutamate and 2-oxo-3-sulfanylpropanoate, underpinning vital metabolic pathways. The enzyme's activity, reliant on the pyridoxal 5'-phosphate cofactor, underscores its significance in cellular energy processes.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of CDGSH iron-sulfur domain-containing protein 1 offers a promising avenue for developing novel therapeutic approaches. Its critical role in metabolic and mitochondrial pathways makes it a compelling target for addressing a spectrum of metabolic and mitochondrial diseases.

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