Focused On-demand Library for Phospholipase A and acyltransferase 4

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 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 use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening 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.

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:

HRAS-like suppressor 4; RAR-responsive protein TIG3; Retinoic acid receptor responder protein 3; Retinoid-inducible gene 1 protein; Tazarotene-induced gene 3 protein

Alternative UPACC:

Q9UL19; B2R599; B4DDW2; E7ENZ7; O95200


Phospholipase A and acyltransferase 4, known by alternative names such as HRAS-like suppressor 4 and Tazarotene-induced gene 3 protein, plays a crucial role in lipid metabolism. It exhibits both phospholipase A1/2 and acyltransferase activities, catalyzing the calcium-independent release of fatty acids and the transfer of fatty acyl groups among glycerophospholipids. Its ability to act on various substrates highlights its versatility in cellular processes.

Therapeutic significance:

Understanding the role of Phospholipase A and acyltransferase 4 could open doors to potential therapeutic strategies. Its involvement in lipid metabolism and cellular processes makes it a promising target for drug discovery, aiming to modulate its activity for therapeutic benefits.

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