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.


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


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


 

Fig. 1. The screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.


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
Receptor.AI
 
UPACC
Q96CG3

UPID:
TIFA_HUMAN

ALTERNATIVE NAMES:
Putative MAPK-activating protein PM14; Putative NF-kappa-B-activating protein 20; TRAF2-binding protein

ALTERNATIVE UPACC:
Q96CG3

BACKGROUND:
The TRAF-interacting protein with FHA domain-containing protein A, known for its alternative names such as TRAF2-binding protein, is crucial in mediating the innate immune response against bacterial infections. It functions by inducing the oligomerization and polyubiquitination of TRAF6, leading to the activation of key signaling pathways such as TAK1 and IKK. This process is initiated by ADP-D-glycero-beta-D-manno-heptose, a bacterial component recognized by ALPK1, which phosphorylates TIFA, facilitating its homooligomerization.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of TRAF-interacting protein with FHA domain-containing protein A could open doors to potential therapeutic strategies.

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