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.


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


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 for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance 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.


PARTNER
Receptor.AI
 
UPACC
O95050

UPID:
INMT_HUMAN

ALTERNATIVE NAMES:
Aromatic alkylamine N-methyltransferase; Thioether S-methyltransferase

ALTERNATIVE UPACC:
O95050; B8ZZ69; Q3KP49; Q9P1Y2; Q9UBY4; Q9UHQ0

BACKGROUND:
The enzyme Indolethylamine N-methyltransferase, known for its alternative names Aromatic alkylamine N-methyltransferase and Thioether S-methyltransferase, exhibits a crucial function in the detoxification of selenium compounds and the N-methylation of tryptamine and similar structures. Its activity with a variety of thioethers, selenium, and tellurium compounds underlines its versatility and essential role in cellular detoxification processes.

THERAPEUTIC SIGNIFICANCE:
The exploration of Indolethylamine N-methyltransferase's functions offers a promising avenue for the development of novel therapeutic approaches. Its key role in detoxification and metabolism presents an opportunity to leverage this enzyme for interventions aimed at mitigating the effects of toxic substances and enhancing metabolic health.

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