Focused On-demand Library for Notch homolog 2 N-terminal-like protein C

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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 top-notch dedicated system is used to design specialised 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 distinguishes itself through several key aspects:


  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.

  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.

  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.

  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

PARTNER
Receptor.AI
 
UPACC
P0DPK4

UPID:
NT2NC_HUMAN

ALTERNATIVE NAMES:
-

ALTERNATIVE UPACC:
P0DPK4; A0A494C1K9

BACKGROUND:
The Notch homolog 2 N-terminal-like protein C is crucial for neural progenitor cell self-renewal and the expansion of the neocortex, acting through the modulation of the Notch signaling pathway. It delays the differentiation of neuronal progenitors, leading to increased neuronal production, by interacting with NOTCH2 and inhibiting DLL1-NOTCH2 interactions.

THERAPEUTIC SIGNIFICANCE:
Given its association with conditions like Neuronal intranuclear inclusion disease, hereditary essential tremor 6, and oculopharyngodistal myopathy 3, the study of Notch homolog 2 N-terminal-like protein C offers promising avenues for therapeutic intervention. Its critical role in neurodevelopment and disease underscores the potential for targeted treatments.

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