Focused On-demand Libraries - Receptor.AI 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.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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


 

Fig. 1. The screening 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.


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
Receptor.AI
 
UPACC
Q9NP80

UPID:
PLPL8_HUMAN

ALTERNATIVE NAMES:
Intracellular membrane-associated calcium-independent phospholipase A2 gamma; PNPLA-gamma; Patatin-like phospholipase domain-containing protein 8; iPLA2-2

ALTERNATIVE UPACC:
Q9NP80; A4D0S1; C9JZI4; O95035; Q8N3I3; Q9H7T5; Q9NR17; Q9NUN2; Q9NZ79

BACKGROUND:
The enzyme Calcium-independent phospholipase A2-gamma, with alternative names such as PNPLA-gamma and iPLA2-2, is integral to phospholipid metabolism. It facilitates the release of fatty acids and lysophospholipids from glycerophospholipids, influencing membrane dynamics and the generation of lipid second messengers. Its specific action on phosphatidylethanolamine and phosphatidylcholine underscores its importance in cellular signaling and mitochondrial function.

THERAPEUTIC SIGNIFICANCE:
Calcium-independent phospholipase A2-gamma's role in mitochondrial myopathy with lactic acidosis highlights its potential as a target for drug discovery. The enzyme's function in lipid metabolism and signaling pathways suggests that modulating its activity could lead to novel treatments for related disorders. Understanding the role of Calcium-independent phospholipase A2-gamma could open doors to potential therapeutic strategies.

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