Focused On-demand Libraries - Receptor.AI 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.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


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.


Our high-tech, dedicated method is applied to construct targeted libraries.


 

Fig. 1. The screening 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 stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q13823

UPID:
NOG2_HUMAN

ALTERNATIVE NAMES:
Autoantigen NGP-1

ALTERNATIVE UPACC:
Q13823; Q9BWN7

BACKGROUND:
The Nucleolar GTP-binding protein 2, known alternatively as Autoantigen NGP-1, is integral to the nuclear export and maturation of pre-60S ribosomal subunits. Its function extends to promoting cell proliferation by potentially increasing p53/TP53 and its downstream product CDKN1A/p21, while decreasing RPL23A protein levels, highlighting its multifaceted role in cellular growth and protein synthesis.

THERAPEUTIC SIGNIFICANCE:
The exploration of Nucleolar GTP-binding protein 2's functions offers a promising avenue for the development of novel therapeutic interventions. Given its pivotal role in ribosome biogenesis and cell proliferation, targeting this protein could lead to breakthroughs in treating conditions characterized by abnormal cell growth.

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