Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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 promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We employ our advanced, specialised process to create targeted libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance activity and selectivity.


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
Q9NRZ9

UPID:
HELLS_HUMAN

ALTERNATIVE NAMES:
Proliferation-associated SNF2-like protein; SWI/SNF2-related matrix-associated actin-dependent regulator of chromatin subfamily A member 6

ALTERNATIVE UPACC:
Q9NRZ9; B2RB41; Q3LID1; Q6I7N7; Q76H76; Q76H77; Q76H78; Q76H79; Q76H80; Q76H81; Q7Z397; Q7Z5X2; Q8N6P4; Q9H4P5

BACKGROUND:
Lymphoid-specific helicase, known for its alternative names such as Proliferation-associated SNF2-like protein, is integral to normal development and survival. It supports de novo or maintenance DNA methylation and plays a role in the silencing of the imprinted CDKN1C gene. Its functions suggest a significant impact on the regulation of lymphoid cells and possibly on the organization of heterochromatin.

THERAPEUTIC SIGNIFICANCE:
The involvement of Lymphoid-specific helicase in Immunodeficiency-centromeric instability-facial anomalies syndrome 4 highlights its potential as a target for therapeutic intervention. The protein's role in DNA methylation and gene regulation presents a promising avenue for developing treatments for this rare genetic disorder and possibly other related diseases.

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