AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Tumor necrosis factor receptor superfamily member 9

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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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 employ our advanced, specialised process to create targeted libraries for receptors.

 Fig. 1. The sreening workflow of Receptor.AI

The method involves detailed molecular simulations of the receptor in its native membrane environment, with ensemble virtual screening focusing on its conformational mobility. When dealing with dimeric or oligomeric receptors, the whole functional complex is modelled, and the tentative binding pockets on and between the subunits are established to address all possible mechanisms of action.

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

Q07011

UPID:

TNR9_HUMAN

Alternative names:

4-1BB ligand receptor; CDw137; T-cell antigen 4-1BB homolog; T-cell antigen ILA

Alternative UPACC:

Q07011

Background:

Tumor necrosis factor receptor superfamily member 9 (TNFRSF9), also known as 4-1BB ligand receptor, CDw137, T-cell antigen 4-1BB homolog, and T-cell antigen ILA, plays a pivotal role in enhancing CD8(+) T-cell survival, cytotoxicity, and mitochondrial activity. This receptor's interaction with TNFSF9/4-1BBL is crucial for promoting immunity against viruses and tumors, highlighting its significance in the immune response.

Therapeutic significance:

TNFRSF9's involvement in Immunodeficiency 109 with lymphoproliferation, a primary immune disorder characterized by recurrent infections and susceptibility to Epstein-Barr virus, underscores its therapeutic potential. Targeting TNFRSF9 could lead to innovative treatments for immune disorders and EBV-related conditions.

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