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.


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.


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.


We use our state-of-the-art dedicated workflow for designing 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
Q96SD1

UPID:
DCR1C_HUMAN

ALTERNATIVE NAMES:
DNA cross-link repair 1C protein; Protein A-SCID; SNM1 homolog C; SNM1-like protein

ALTERNATIVE UPACC:
Q96SD1; D3DRT6; Q1HCL2; Q5JSR4; Q5JSR5; Q5JSR7; Q5JSR8; Q5JSR9; Q5JSS0; Q5JSS7; Q6PK14; Q8N101; Q8N132; Q8TBW9; Q9BVW9; Q9HAM4

BACKGROUND:
The Protein Artemis, alternatively named SNM1 homolog C, is integral to the V(D)J recombination and DNA repair mechanisms. Its activity includes single-strand specific 5'-3' exonuclease and endonucleolytic activity, crucial for the immune system's development by facilitating the assembly of antigen-binding domains.

THERAPEUTIC SIGNIFICANCE:
Mutations in the Artemis gene cause severe immunodeficiencies, such as various forms of severe combined immunodeficiency (SCID) and Omenn syndrome. Targeting the molecular pathways involving Protein Artemis offers a promising avenue for developing novel treatments for these disorders.

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