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 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
Q8IY81

UPID:
SPB1_HUMAN

ALTERNATIVE NAMES:
Protein ftsJ homolog 3; Putative rRNA methyltransferase 3

ALTERNATIVE UPACC:
Q8IY81; B2RCA5; D3DU22; Q8N3A3; Q8WXX1; Q9BWM4; Q9NXT6

BACKGROUND:
FTSJ3, identified for its role in RNA 2'-O-methylation, is crucial for the conversion of 34S pre-rRNA into 18S rRNA, facilitating the assembly of the 40S ribosomal subunit. During HIV-1 infection, FTSJ3 is hijacked to methylate the viral genome, aiding the virus in evading the innate immune system by mimicking cellular RNA.

THERAPEUTIC SIGNIFICANCE:
The unique function of FTSJ3 in both cellular RNA processing and viral evasion mechanisms highlights its potential as a therapeutic target. Exploring FTSJ3's role could lead to novel strategies for treating diseases linked to ribosomal dysfunction and viral infections.

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