AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Recombining binding protein suppressor of hairless

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

We employ our advanced, specialised process to create 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

Q06330

UPID:

SUH_HUMAN

Alternative names:

CBF-1; J kappa-recombination signal-binding protein; RBP-J kappa; Renal carcinoma antigen NY-REN-30

Alternative UPACC:

Q06330; B4DY22; Q5XKH9; Q6P1N3

Background:

The Recombining binding protein suppressor of hairless, known by alternative names such as CBF-1, RBP-J kappa, plays a pivotal role in Notch signaling. This pathway is crucial for cell-cell communication, influencing a wide range of cell fate decisions. It functions as a transcriptional repressor or activator, depending on its association with Notch proteins, and is involved in various cellular processes including DNA binding and hypoxia response.

Therapeutic significance:

Linked to Adams-Oliver syndrome 3, a genetic condition marked by skin and limb abnormalities, understanding the role of this protein could open doors to potential therapeutic strategies. Its involvement in Notch signaling pathways offers a promising target for addressing the syndrome's manifestations.

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