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.


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


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve 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
Q9Y508

UPID:
RN114_HUMAN

ALTERNATIVE NAMES:
RING finger protein 114; RING-type E3 ubiquitin transferase RNF114; Zinc finger protein 228; Zinc finger protein 313

ALTERNATIVE UPACC:
Q9Y508; B2RDQ9; B4DWY5; E1P627; Q6N0B0

BACKGROUND:
The E3 ubiquitin-protein ligase RNF114, known alternatively as Zinc finger protein 228 and 313, is integral to regulating cell cycle, apoptosis, and immune system functioning. By promoting ubiquitination, RNF114 negatively regulates NF-kappa-B transcription and T-cell activation, and it plays a role in osteoclastogenesis and innate or adaptive immunity. Its activity against MAVS, TNFAIP3, TRAF6, and CDK inhibitors like CDKN1A underscores its significance in cellular processes and potential in influencing cell cycle transitions and cellular aging.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of E3 ubiquitin-protein ligase RNF114 could open doors to potential therapeutic strategies.

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