Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed 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 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.


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
Q01844

UPID:
EWS_HUMAN

ALTERNATIVE NAMES:
EWS oncogene; Ewing sarcoma breakpoint region 1 protein

ALTERNATIVE UPACC:
Q01844; B0QYK1; Q5THL0; Q92635; Q96FE8; Q96MN4; Q96MX4; Q9BWA2

BACKGROUND:
RNA-binding protein EWS functions as a transcriptional repressor and is involved in the formation of EWS-fusion proteins (EFPs) that may contribute to cancer development. These fusion proteins can interfere with normal transcription initiation processes and lead to the aberrant activation of genes.

THERAPEUTIC SIGNIFICANCE:
The direct involvement of the RNA-binding protein EWS in the pathogenesis of Ewing sarcoma and Angiomatoid fibrous histiocytoma, through the formation of oncogenic fusion proteins, underscores its potential as a target for innovative therapeutic interventions. Exploring the mechanisms by which EWS contributes to these diseases could pave the way for novel treatment options.

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