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 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.


Our top-notch dedicated system is used to design specialised 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
Q96AQ7

UPID:
CIDEC_HUMAN

ALTERNATIVE NAMES:
Cell death activator CIDE-3; Cell death-inducing DFFA-like effector protein C; Fat-specific protein FSP27 homolog

ALTERNATIVE UPACC:
Q96AQ7; C9JMN7; Q67DW9; Q9GZY9

BACKGROUND:
The Lipid transferase CIDEC, known for its alternative names such as Fat-specific protein FSP27 homolog, is integral to lipid droplet enlargement in white adipose tissue. By mediating lipid droplet fusion, CIDEC restricts lipolysis and favors lipid storage, a key mechanism for energy conservation. Its interaction with PLIN1 activates its role in neutral lipid transfer, highlighting its significance in lipid metabolism regulation.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Lipid transferase CIDEC could open doors to potential therapeutic strategies. Given its critical function in lipid metabolism and its association with Lipodystrophy, familial partial, 5, targeting CIDEC offers a promising approach to treat metabolic diseases by modulating lipid storage and distribution.

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