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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed 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.


We utilise our cutting-edge, exclusive workflow to develop focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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
Q15784

UPID:
NDF2_HUMAN

ALTERNATIVE NAMES:
Class A basic helix-loop-helix protein 1; NeuroD-related factor

ALTERNATIVE UPACC:
Q15784; Q8TBI7; Q9UQC6

BACKGROUND:
The protein Neurogenic differentiation factor 2, known for its alternative names Class A basic helix-loop-helix protein 1 and NeuroD-related factor, is a key transcriptional regulator in neuronal determination. It prevents synaptic vesicle clustering at the presynaptic membrane in postmitotic neurons and plays a significant role in the maturation of thalamocortical connections and the segregation of thalamic afferents.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Neurogenic differentiation factor 2 offers a promising avenue for the development of therapeutic interventions for Developmental and epileptic encephalopathy 72, highlighting its potential in addressing the underlying genetic variants affecting neurodevelopment and seizure disorders.

Looking for more information on this library or underlying technology? Fill out the form below and we will be in touch with all the details you need.