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 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 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 utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.


Our library is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
P78417

UPID:
GSTO1_HUMAN

ALTERNATIVE NAMES:
Glutathione S-transferase omega 1-1; Glutathione-dependent dehydroascorbate reductase; Monomethylarsonic acid reductase; S-(Phenacyl)glutathione reductase

ALTERNATIVE UPACC:
P78417; D3DRA3; F5H7H0; Q5TA03; Q7Z3T2

BACKGROUND:
The enzyme Glutathione S-transferase omega-1, identified by the protein accession number P78417, is crucial for cellular detoxification. It possesses glutathione S-transferase activity, playing a significant role in the metabolism of toxins and carcinogens. The enzyme's ability to reduce monomethylarsonic acid and dimethylarsonic acid highlights its importance in arsenic detoxification, a critical process for preventing arsenic-related toxicity and carcinogenesis.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Glutathione S-transferase omega-1 offers promising avenues for drug discovery, particularly in developing treatments for arsenic poisoning and reducing the risk of arsenic-related diseases. Its enzymatic activities present a foundation for therapeutic interventions aimed at enhancing detoxification pathways.

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