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.


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


The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We employ our advanced, specialised process to create targeted libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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
Q13562

UPID:
NDF1_HUMAN

ALTERNATIVE NAMES:
Class A basic helix-loop-helix protein 3

ALTERNATIVE UPACC:
Q13562; B2R9I8; F1T0E1; O00343; Q13340; Q5U095; Q96TH0; Q99455; Q9UEC8

BACKGROUND:
The protein Neurogenic differentiation factor 1, with its alternative name Class A basic helix-loop-helix protein 3, is crucial for the transcriptional activation of genes involved in cell differentiation and neurogenesis. It associates with transcription coactivator complexes to stimulate gene expression, playing a key role in the development of retinal ganglion cells, sensory neurons, and pancreatic endocrine islet cells.

THERAPEUTIC SIGNIFICANCE:
Given its involvement in Maturity-onset diabetes of the young 6 and Type 2 diabetes mellitus, Neurogenic differentiation factor 1 presents a promising target for therapeutic intervention. Its role in insulin secretion and cell differentiation pathways offers potential pathways for the development of treatments for these metabolic conditions.

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