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 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.


The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


Our high-tech, dedicated method is applied to construct 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.


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.


PARTNER
Receptor.AI
 
UPACC
Q6QHK4

UPID:
FIGLA_HUMAN

ALTERNATIVE NAMES:
Class C basic helix-loop-helix protein 8; Folliculogenesis-specific basic helix-loop-helix protein; Transcription factor FIGa

ALTERNATIVE UPACC:
Q6QHK4

BACKGROUND:
The transcription factor FIGa, also known as Factor in the germline alpha, plays a key role in postnatal oocyte-specific gene expression. Essential for the initiation of folliculogenesis and encoding of zona pellucida proteins (ZP1, ZP2, ZP3), FIGa is crucial for fertilization and embryonic survival. Its continued presence in adult ovaries suggests roles in additional ovarian developmental pathways.

THERAPEUTIC SIGNIFICANCE:
Linked to Premature ovarian failure 6, a disorder characterized by the early loss of ovarian function, FIGa's genetic variants highlight its importance. Exploring FIGa's function opens pathways to potential therapeutic interventions for ovarian health.

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