Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We use our state-of-the-art dedicated workflow for designing focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.


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
Q9P0L9

UPID:
PK2L1_HUMAN

ALTERNATIVE NAMES:
Polycystic kidney disease 2-like 1 protein; Polycystin-2 homolog; Polycystin-L; Polycystin-L1

ALTERNATIVE UPACC:
Q9P0L9; O75972; Q5W039; Q9UP35; Q9UPA2

BACKGROUND:
The Polycystin-2-like protein 1, with alternative names such as Polycystin-L and Polycystin-2 homolog, is integral to cellular signaling and sensory perception. It forms part of a channel that controls cilium calcium concentration, influencing sonic hedgehog/SHH signaling and GLI2 transcription, without affecting cytoplasmic calcium levels. This protein's ability to form channels with PKD1L2 and PKD1L3 plays a significant role in the perception of sour taste and possibly carbonation, by responding to changes in pH levels.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Polycystin-2-like protein 1 could open doors to potential therapeutic strategies.

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