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.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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 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 is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
Q6P4F2

UPID:
FDX2_HUMAN

ALTERNATIVE NAMES:
Adrenodoxin-like protein; Ferredoxin-1-like protein

ALTERNATIVE UPACC:
Q6P4F2; B7Z6L7; Q8N8B8

BACKGROUND:
The mitochondrial protein Ferredoxin-2, also referred to as Adrenodoxin-like or Ferredoxin-1-like protein, is integral to the de novo synthesis of [2Fe-2S] clusters. These clusters are vital for mitochondrial function, acting in the first step of mitochondrial iron-sulfur protein biogenesis. Ferredoxin-2's role includes electron donation in the ISC assembly complex, a critical step for the assembly of these clusters on the scaffolding protein ISCU.

THERAPEUTIC SIGNIFICANCE:
Linked to a rare neuromuscular disorder, Ferredoxin-2's dysfunction manifests in Mitochondrial myopathy with episodic weakness and optic atrophy. Delving into Ferredoxin-2's function offers a promising avenue for developing treatments for mitochondrial disorders, highlighting its therapeutic significance.

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