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 pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are 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 employ our advanced, specialised process to create targeted libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.


PARTNER
Receptor.AI
 
UPACC
Q9H2G2

UPID:
SLK_HUMAN

ALTERNATIVE NAMES:
CTCL tumor antigen se20-9; STE20-related serine/threonine-protein kinase; Serine/threonine-protein kinase 2

ALTERNATIVE UPACC:
Q9H2G2; D3DRA0; D3DRA1; O00211; Q6P1Z4; Q86WU7; Q86WW1; Q92603; Q9NQL0; Q9NQL1

BACKGROUND:
STE20-like serine/threonine-protein kinase, known for its alternative names such as CTCL tumor antigen se20-9, plays a pivotal role in cellular apoptosis and actin stress fiber breakdown. This protein's activity is essential for maintaining cellular integrity and responding to stress signals.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of STE20-like serine/threonine-protein kinase offers promising avenues for therapeutic intervention. Given its critical role in apoptosis and cellular structure maintenance, targeting this kinase could lead to innovative treatments for diseases where these processes are dysregulated.

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