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.


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


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


We use our state-of-the-art dedicated workflow for designing focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast 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
Q9UBE0

UPID:
SAE1_HUMAN

ALTERNATIVE NAMES:
Ubiquitin-like 1-activating enzyme E1A

ALTERNATIVE UPACC:
Q9UBE0; B2RDP5; B3KMQ2; F5GXX7; G3XAK6; O95717; Q9P020

BACKGROUND:
The enzyme SUMO-activating enzyme subunit 1, alternatively named Ubiquitin-like 1-activating enzyme E1A, is essential for the SUMOylation pathway, mediating the activation of SUMO proteins. This process is crucial for the post-translational modification of proteins, involving ATP-dependent activation and subsequent conjugation of SUMO proteins to specific substrates.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of SUMO-activating enzyme subunit 1 offers a promising avenue for drug discovery. Given its central role in protein SUMOylation, targeting this enzyme could lead to innovative treatments for diseases where protein regulation is disrupted.

Looking for more information on this library or underlying technology? Fill out the form below and we will be in touch with all the details you need.