Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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 for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.


Our library is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
Q9NV23

UPID:
SAST_HUMAN

ALTERNATIVE NAMES:
Augmented in rheumatoid arthritis 1; Oleoyl-ACP hydrolase; Thioesterase 2; Thioesterase II; Thioesterase domain-containing protein 1

ALTERNATIVE UPACC:
Q9NV23; Q5VUB6; Q9NUW1

BACKGROUND:
S-acyl fatty acid synthase thioesterase, medium chain, also referred to as Thioesterase II or Oleoyl-ACP hydrolase, is essential for lipid biosynthesis. It contributes significantly to the generation of free fatty acids from fatty acid synthase, with a preference for fatty acids having chain lengths between C10 and C16. This enzyme's broad substrate specificity underscores its vital role in cellular lipid metabolism.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionality of S-acyl fatty acid synthase thioesterase, medium chain, offers a promising avenue for identifying novel therapeutic approaches. Its critical involvement in the production of free fatty acids highlights its potential as a target for addressing metabolic diseases.

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