AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Peroxisomal trans-2-enoyl-CoA reductase

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner Reaxense.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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

 Fig. 1. The sreening 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.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

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:

Peroxisomal trans-2-enoyl-CoA reductase, known by alternative names such as 2,4-dienoyl-CoA reductase-related protein, HPDHase, and Short chain dehydrogenase/reductase family 29C member 1, plays a crucial role in fatty acid metabolism. It specifically catalyzes the reduction of trans-2-enoyl-CoAs with chain lengths ranging from 6:1 to 16:1, exhibiting peak activity with 10:1 CoA. This enzyme is pivotal in the chain elongation process of fatty acids, a fundamental biochemical pathway.

Therapeutic significance:

Understanding the role of Peroxisomal trans-2-enoyl-CoA reductase could open doors to potential therapeutic strategies. Its critical function in fatty acid metabolism makes it a compelling target for research aimed at treating metabolic disorders.

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