Focused On-demand Library for Interferon regulatory factor 4

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 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 sreening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance activity and selectivity.

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.







Alternative names:

Lymphocyte-specific interferon regulatory factor; Multiple myeloma oncogene 1; NF-EM5

Alternative UPACC:

Q15306; Q5VUI7; Q99660


Interferon regulatory factor 4 (IRF4), also known as Multiple myeloma oncogene 1 and NF-EM5, plays a pivotal role in immune response regulation. It acts as a transcriptional activator, binding to specific elements of the MHC class I promoter and the immunoglobulin lambda light chain enhancer. Its involvement in CD8(+) dendritic cell differentiation highlights its significance in lymphoid cell-specific signal transduction pathways.

Therapeutic significance:

IRF4's aberration, particularly the translocation t(6;14)(p25;q32) with the IgH locus, is implicated in multiple myeloma, a malignant plasma cell tumor. This association underscores the potential of targeting IRF4 in therapeutic strategies aimed at treating multiple myeloma and possibly other related hematological malignancies.

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