Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.


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
Q14527

UPID:
HLTF_HUMAN

ALTERNATIVE NAMES:
DNA-binding protein/plasminogen activator inhibitor 1 regulator; HIP116; RING finger protein 80; RING-type E3 ubiquitin transferase HLTF; SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A member 3; Sucrose nonfermenting protein 2-like 3

ALTERNATIVE UPACC:
Q14527; D3DNH3; Q14536; Q16051; Q7KYJ6; Q86YA5; Q92652; Q96KM9

BACKGROUND:
Helicase-like transcription factor, known by alternative names such as DNA-binding protein/plasminogen activator inhibitor 1 regulator and RING-type E3 ubiquitin transferase HLTF, exhibits intrinsic ATP-dependent nucleosome-remodeling activity. This activity is essential for the transcriptional activation or repression of target promoters and maintaining genomic stability through its role in error-free postreplication repair of damaged DNA.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Helicase-like transcription factor offers a promising avenue for developing novel therapeutic approaches. Its critical role in maintaining genomic integrity and regulating gene expression makes it a potential target in designing treatments for diseases characterized by genomic instability and aberrant transcription.

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