AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Transcriptional regulator ERG

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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 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 our associate 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.

Our high-tech, dedicated method is applied to construct targeted libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide 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

Reaxense

upacc

P11308

UPID:

ERG_HUMAN

Alternative names:

Transforming protein ERG

Alternative UPACC:

P11308; B4DTW5; B4E0T4; Q16113; Q6XXX4; Q6XXX5; Q8IXK9

Background:

The Transcriptional regulator ERG, also known as Transforming protein ERG, encoded by the gene with accession number P11308, plays a pivotal role in gene expression regulation. It achieves this through its involvement in transcriptional regulation, notably by recruiting the SETDB1 histone methyltransferase, which alters local chromatin structure.

Therapeutic significance:

ERG's involvement in Ewing sarcoma, a highly malignant tumor affecting children and adolescents, underscores its therapeutic significance. The chromosomal aberration involving ERG, specifically the translocation t(21;22)(q22;q12) with EWSR1, highlights its role in disease pathogenesis. Targeting ERG's function or its aberrant expression could offer novel therapeutic strategies for Ewing sarcoma.

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