AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Lipid transferase CIDEC

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced activity, selectivity, and safety.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

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

 Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds 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

Reaxense

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:

Lipid transferase CIDEC, also known as Cell death activator CIDE-3, plays a pivotal role in lipid metabolism within white adipose tissue. It facilitates the formation of unilocular lipid droplets by mediating their fusion, thus promoting lipid storage over lipolysis. This process is crucial for maintaining energy balance and metabolic health. CIDEC localizes on the lipid droplet surface, engaging in atypical lipid droplet fusion through liquid-liquid phase separation and directional net neutral lipid transfer.

Therapeutic significance:

CIDEC's involvement in Lipodystrophy, familial partial, 5, a condition characterized by abnormal fat distribution and metabolic complications, underscores its therapeutic potential. Targeting CIDEC's pathway could offer novel interventions for managing lipodystrophy and related metabolic disorders, providing a promising avenue for drug discovery aimed at restoring lipid homeostasis and improving insulin sensitivity.

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