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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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 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 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
Q9NUZ1

UPID:
ACOXL_HUMAN

ALTERNATIVE NAMES:
-

ALTERNATIVE UPACC:
Q9NUZ1; A2RRB7; B7WPB3; B7WPP7; E9PB20; Q53R27; Q53R31; Q53SC6; Q8TCE7

BACKGROUND:
The Acyl-coenzyme A oxidase-like protein, with the unique identifier Q9NUZ1, is integral to cellular fatty acid degradation. This protein's function is essential for initiating the breakdown of fatty acids, facilitating their conversion into energy, and thus playing a significant role in maintaining cellular energy homeostasis.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Acyl-coenzyme A oxidase-like protein holds significant promise for uncovering new therapeutic approaches. Given its central role in energy metabolism, targeting this protein could lead to innovative treatments for diseases related to energy dysregulation and metabolic disorders.

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