Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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
Q8N568

UPID:
DCLK2_HUMAN

ALTERNATIVE NAMES:
CaMK-like CREB regulatory kinase 2; Doublecortin domain-containing protein 3B; Doublecortin-like and CAM kinase-like 2; Doublecortin-like kinase 2

ALTERNATIVE UPACC:
Q8N568; C9J5Q9; Q59GC8; Q8N399

BACKGROUND:
The protein known as Doublecortin-like kinase 2, with alternative names such as Doublecortin domain-containing protein 3B, plays a pivotal role in the modulation of gene activation through its kinase activity. This activity leads to the phosphorylation and cytoplasmic retention of CRTC2/TORC2, a key coactivator in CREB-mediated gene expression.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Doublecortin-like kinase 2 offers a promising avenue for the development of novel therapeutic approaches. Its involvement in critical signaling pathways suggests its potential as a therapeutic target in conditions where these pathways are implicated.

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