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 utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.


Our library is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
Q9BY49

UPID:
PECR_HUMAN

ALTERNATIVE NAMES:
2,4-dienoyl-CoA reductase-related protein; HPDHase; Short chain dehydrogenase/reductase family 29C member 1; pVI-ARL

ALTERNATIVE UPACC:
Q9BY49; B2RE42; Q53TC4; Q6IAK9; Q9NRD4; Q9NY60; Q9P1A4

BACKGROUND:
The enzyme Peroxisomal trans-2-enoyl-CoA reductase, also referred to as HPDHase and Short chain dehydrogenase/reductase family 29C member 1, is integral to the process of fatty acid chain elongation. It efficiently reduces trans-2-enoyl-CoAs across a variety of chain lengths, with optimal activity observed for 10:1 CoA substrates. This specificity underscores its essential role in the metabolic pathways of fatty acids.

THERAPEUTIC SIGNIFICANCE:
Exploring the function of Peroxisomal trans-2-enoyl-CoA reductase holds promise for unveiling new therapeutic avenues. Given its central role in fatty acid metabolism, targeting this enzyme could lead to innovative treatments for metabolic diseases.

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