Focused On-demand Library for Tapasin

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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

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.

 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.

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.







Alternative names:

NGS-17; TAP-associated protein; TAP-binding protein

Alternative UPACC:

O15533; A2AB91; A2ABC0; B0V003; B0V0A6; B2ZUA4; E9PGM2; O15210; O15272; Q5STJ8; Q5STK6; Q5STQ5; Q5STQ6; Q66K65; Q96KK7; Q9HAN8; Q9UEE0; Q9UEE4; Q9UIZ6; Q9Y6K2


Tapasin, also known as NGS-17 or TAP-binding protein, plays a crucial role in immune response. It facilitates the association of MHC class I molecules with the transporter associated with antigen processing (TAP) and assists in the assembly of MHC class I with peptides, a process vital for antigen presentation.

Therapeutic significance:

Tapasin's involvement in Bare Lymphocyte Syndrome 1, characterized by chronic respiratory infections due to HLA class I deficiency, underscores its therapeutic potential. Enhancing Tapasin function could offer new strategies for treating this immune disorder.

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