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.


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 top-notch dedicated system is used to design specialised libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse 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
Q7Z628

UPID:
ARHG8_HUMAN

ALTERNATIVE NAMES:
Proto-oncogene p65 Net1; Rho guanine nucleotide exchange factor 8

ALTERNATIVE UPACC:
Q7Z628; Q12773; Q96D82; Q99903; Q9UEN6

BACKGROUND:
The Neuroepithelial cell-transforming gene 1 protein, known alternatively as Proto-oncogene p65 Net1 and Rho guanine nucleotide exchange factor 8, is key in regulating cellular dynamics. It functions as a guanine nucleotide exchange factor for RhoA GTPase, essential for activating the SAPK/JNK pathway. Additionally, it plays a significant role in inducing RHOB activity in response to genotoxic stress in breast cancer cells, facilitating their apoptosis.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Neuroepithelial cell-transforming gene 1 protein unveils potential avenues for therapeutic intervention. Its critical role in modulating pathways involved in cancer cell apoptosis positions it as a promising candidate for the development of targeted cancer treatments.

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