Focused On-demand Library for b(0,+)-type amino acid transporter 1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of 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.







Alternative names:

Glycoprotein-associated amino acid transporter b0,+AT1; Solute carrier family 7 member 9

Alternative UPACC:

P82251; B2R9A6


The b(0,+)-type amino acid transporter 1, also known as Solute carrier family 7 member 9, plays a crucial role in the transport of cationic amino acids and L-cystine across cell membranes. This protein forms a functional transporter complex with SLC3A1, facilitating the exchange of extracellular cationic amino acids and L-cystine for intracellular neutral amino acids. Its activity is essential for the reabsorption of L-cystine and dibasic amino acids in renal proximal tubules.

Therapeutic significance:

Mutations in the gene encoding b(0,+)-type amino acid transporter 1 are linked to Cystinuria, a disorder characterized by the formation of cystine stones in the urinary tract. Understanding the role of this transporter could open doors to potential therapeutic strategies for managing Cystinuria, aiming to enhance the reabsorption of cystine and prevent stone formation.

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