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.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We utilise our cutting-edge, exclusive workflow to develop 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.


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
O60662

UPID:
KLH41_HUMAN

ALTERNATIVE NAMES:
Kel-like protein 23; Kelch repeat and BTB domain-containing protein 10; Kelch-related protein 1; Sarcosin

ALTERNATIVE UPACC:
O60662; Q53R42

BACKGROUND:
The Kelch-like protein 41, with alternative names such as Kel-like protein 23 and Sarcosin, is crucial for skeletal muscle development. It regulates myoblast proliferation and differentiation and is vital for myofibril assembly, ensuring the proper formation of muscle fibers. Additionally, it plays a significant role in cell morphology, aiding in pseudopod elongation in transformed cells.

THERAPEUTIC SIGNIFICANCE:
Linked to Nemaline myopathy 9, a condition marked by severe muscle weakness and histological abnormalities in muscle fibers, Kelch-like protein 41's involvement in this disease underscores its therapeutic potential. Exploring the functions and mechanisms of Kelch-like protein 41 could lead to innovative treatments for muscle-related disorders, making it a key target in drug discovery efforts.

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