Focused On-demand Libraries - Receptor.AI 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.


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 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

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve 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
Q9H4B4

UPID:
PLK3_HUMAN

ALTERNATIVE NAMES:
Cytokine-inducible serine/threonine-protein kinase; FGF-inducible kinase; Polo-like kinase 3; Proliferation-related kinase

ALTERNATIVE UPACC:
Q9H4B4; Q15767; Q5JR99; Q96CV1

BACKGROUND:
The Serine/threonine-protein kinase PLK3, with alternative names such as Polo-like kinase 3 and Proliferation-related kinase, is integral to cell cycle control, stress response, and Golgi apparatus disassembly. It phosphorylates a range of proteins including ATF2, BCL2L1, and p53/TP53, playing a key role in DNA damage response, G1/S transition, and apoptosis. Its activation is crucial for managing cellular responses to environmental stresses, including oxidative stress and DNA damage.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Serine/threonine-protein kinase PLK3 holds promise for unveiling novel therapeutic avenues.

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