Focused On-demand Library for Tissue alpha-L-fucosidase

Details

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.


We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.


 

Fig. 1. The screening 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 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
P04066

UPID:
FUCO_HUMAN

ALTERNATIVE NAMES:
Alpha-L-fucosidase I; Alpha-L-fucoside fucohydrolase 1

ALTERNATIVE UPACC:
P04066; B2RBG3; Q14334; Q14335; Q3LID0; Q8NAC2

BACKGROUND:
Tissue alpha-L-fucosidase, with alternative names Alpha-L-fucosidase I and Alpha-L-fucoside fucohydrolase 1, is encoded by the gene with the UniProt accession P04066. This enzyme is pivotal in the catabolism of glycoproteins, specifically targeting the alpha-1,6-linked fucose residues. Its activity is essential for maintaining the balance of glycoprotein turnover in cells, facilitating the breakdown and removal of glycoproteins that are no longer needed.

THERAPEUTIC SIGNIFICANCE:
Alterations in the function of Tissue alpha-L-fucosidase are implicated in the development of Fucosidosis, a rare lysosomal storage disorder. Patients with Fucosidosis exhibit a range of clinical manifestations, including significant intellectual deficits, skeletal abnormalities, and liver enlargement. The connection between Tissue alpha-L-fucosidase and Fucosidosis provides valuable insights for potential therapeutic interventions.

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