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.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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
Q9UGN5

UPID:
PARP2_HUMAN

ALTERNATIVE NAMES:
ADP-ribosyltransferase diphtheria toxin-like 2; DNA ADP-ribosyltransferase PARP2; NAD(+) ADP-ribosyltransferase 2; Poly[ADP-ribose] synthase 2; Protein poly-ADP-ribosyltransferase PARP2

ALTERNATIVE UPACC:
Q9UGN5; Q8TEU4; Q9NUV2; Q9UMR4; Q9Y6C8

BACKGROUND:
Poly [ADP-ribose] polymerase 2, known for its roles in DNA repair, operates through poly-ADP-ribosylation, a process vital for cellular response to DNA damage. It specifically mediates the formation of branched poly-ADP-ribose chains, recognized by repair factors, thereby playing a key role in the repair of double-strand DNA breaks and maintaining genomic stability.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Poly [ADP-ribose] polymerase 2 offers promising avenues for developing novel therapeutic approaches.

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