Focused On-demand Library for Long-chain-fatty-acid--CoA ligase 4

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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner 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 employ our advanced, specialised process to create targeted 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.

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.







Alternative names:

Arachidonate--CoA ligase; Long-chain acyl-CoA synthetase 4

Alternative UPACC:

O60488; D3DUY2; O60848; O60849; Q5JWV8


Long-chain-fatty-acid--CoA ligase 4, also known as Arachidonate--CoA ligase and Long-chain acyl-CoA synthetase 4, plays a crucial role in lipid metabolism. It catalyzes the conversion of long-chain fatty acids into their active form, acyl-CoA, facilitating both the synthesis of cellular lipids and their degradation via beta-oxidation. This enzyme shows a preference for arachidonate and eicosapentaenoate as substrates, significantly influencing the modulation of glucose-stimulated insulin secretion and prostaglandin E2 levels.

Therapeutic significance:

Long-chain-fatty-acid--CoA ligase 4 is implicated in Intellectual developmental disorder, X-linked 63, and AMME complex, diseases characterized by intellectual disability among other symptoms. Understanding the role of this protein could lead to novel therapeutic strategies targeting these X-linked disorders, potentially offering new hope for patients.

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