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.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is 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 employ our advanced, specialised process to create targeted 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
Q6XPS3

UPID:
TPTE2_HUMAN

ALTERNATIVE NAMES:
Lipid phosphatase TPIP; TPTE and PTEN homologous inositol lipid phosphatase

ALTERNATIVE UPACC:
Q6XPS3; A1A4X0; A1A4X1; A8MX64; B1AQ16; B4DWZ2; Q5VUH2; Q8WWL4; Q8WWL5

BACKGROUND:
The protein Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase TPTE2, with alternative names Lipid phosphatase TPIP and TPTE and PTEN homologous inositol lipid phosphatase, is integral to lipid signaling pathways. It uniquely targets the D3 position phosphate on phosphatidylinositol 3,4,5-trisphosphate, despite showing no general phosphoinositide phosphatase activity.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase TPTE2 holds promise for unveiling novel therapeutic avenues.

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