AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Lipopolysaccharide-induced tumor necrosis factor-alpha factor

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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 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 use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

Q99732

UPID:

LITAF_HUMAN

Alternative names:

Small integral membrane protein of lysosome/late endosome; p53-induced gene 7 protein

Alternative UPACC:

Q99732; D3DUG1; G5E9K0; Q05DW0; Q9C0L6

Background:

The Lipopolysaccharide-induced tumor necrosis factor-alpha factor, also known as Small integral membrane protein of lysosome/late endosome or p53-induced gene 7 protein, plays a crucial role in endosomal protein trafficking and lysosomal degradation. It targets endocytosed EGFR and ERGG3 for lysosomal degradation, regulating downstream signaling cascades. Additionally, it facilitates the recruitment of ESCRT complex components to cytoplasmic membranes and interacts with NEDD4 to regulate protein degradation. This protein also contributes to gene expression regulation in the nucleus and may bind DNA, playing a role in cytokine expression regulation.

Therapeutic significance:

Given its involvement in Charcot-Marie-Tooth disease, demyelinating, 1C, understanding the role of Lipopolysaccharide-induced tumor necrosis factor-alpha factor could open doors to potential therapeutic strategies for this peripheral nervous system disorder. Its function in protein trafficking and degradation pathways offers a promising target for developing treatments aimed at modulating these processes in disease contexts.

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