Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are 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

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.


Our library distinguishes itself through several key aspects:


  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.

  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.

  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.

  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q9NST1

UPID:
PLPL3_HUMAN

ALTERNATIVE NAMES:
Acylglycerol transacylase; Adiponutrin; Calcium-independent phospholipase A2-epsilon; Lysophosphatidic acid acyltransferase; Patatin-like phospholipase domain-containing protein 3

ALTERNATIVE UPACC:
Q9NST1; B0QYI0; B2RCL3; B3KW00; Q6P1A1; Q96CB4

BACKGROUND:
The protein 1-acylglycerol-3-phosphate O-acyltransferase PNPLA3, with alternative names such as Adiponutrin and Calcium-independent phospholipase A2-epsilon, is pivotal in lipid biosynthesis. It specifically acts on 1-acyl-sn-glycerol 3-phosphate, facilitating the production of phosphatidic acid, and indirectly influences the synthesis of triglycerides and glycerophospholipids.

THERAPEUTIC SIGNIFICANCE:
Given its involvement in Non-alcoholic fatty liver disease (NAFLD), PNPLA3 represents a significant target for therapeutic intervention. The enzyme's role in lipid accumulation and metabolic dysregulation in the liver highlights its potential as a focal point for developing treatments aimed at NAFLD and its progression to more severe liver conditions.

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