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.


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.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


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
O76054

UPID:
S14L2_HUMAN

ALTERNATIVE NAMES:
Alpha-tocopherol-associated protein; Squalene transfer protein; Supernatant protein factor

ALTERNATIVE UPACC:
O76054; B7Z8Q1; F5H3U4; Q53EQ2; Q6PD61; Q9ULN4

BACKGROUND:
The SEC14-like protein 2, known for its alternative names such as Squalene transfer protein and Supernatant protein factor, is integral to the intracellular transport of hydrophobic molecules. It binds with high affinity to alpha-tocopherol, enhancing its transfer across cellular sites, and exhibits transcriptional activatory activity. This protein's ability to bind squalene suggests a significant role in modulating cholesterol biosynthesis, highlighting its importance in cellular metabolism.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of SEC14-like protein 2 offers a promising avenue for developing novel therapeutic interventions. Given its critical role in molecule transport and cholesterol regulation, targeting this protein could lead to breakthroughs in treating diseases related to cholesterol metabolism and vitamin E utilization.

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