Focused On-demand Libraries - Receptor.AI 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.


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 promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


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


 

Fig. 1. The screening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.


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
Q9NZ01

UPID:
TECR_HUMAN

ALTERNATIVE NAMES:
Synaptic glycoprotein SC2; Trans-2,3-enoyl-CoA reductase

ALTERNATIVE UPACC:
Q9NZ01; B2RD55; O75350; Q6IBB2; Q9BWK3; Q9Y6P0

BACKGROUND:
Very-long-chain enoyl-CoA reductase, known alternatively as Synaptic glycoprotein SC2 and Trans-2,3-enoyl-CoA reductase, is integral to the synthesis and breakdown of VLCFAs, crucial for sphingolipid production and the sphingosine 1-phosphate pathway. It catalyzes the final reaction in the fatty acid elongation cycle, contributing to the production of VLCFAs that serve as precursors for membrane lipids and lipid mediators.

THERAPEUTIC SIGNIFICANCE:
The enzyme's involvement in Intellectual developmental disorder, autosomal recessive 14, underscores its therapeutic potential. Exploring Very-long-chain enoyl-CoA reductase's role could unveil new therapeutic strategies, highlighting the importance of understanding its biological functions.

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