Focused On-demand Library for Nuclear receptor subfamily 4 group A member 3

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 pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

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.

Our high-tech, dedicated method is applied to construct targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

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:

Mitogen-induced nuclear orphan receptor; Neuron-derived orphan receptor 1; Nuclear hormone receptor NOR-1

Alternative UPACC:

Q92570; A2A3I7; Q12935; Q14979; Q16420; Q4VXA8; Q4VXA9; Q9UEK2; Q9UEK3


Nuclear receptor subfamily 4 group A member 3 (NR4A3), also known as Neuron-derived orphan receptor 1, plays a pivotal role in various cellular processes including proliferation, survival, differentiation, metabolism, and inflammation. It functions by binding to specific DNA sequences, regulating gene expression in response to physiological stimuli. NR4A3's involvement in cell cycle regulation and survival, particularly in vascular smooth muscle and neuronal cells, underscores its biological significance.

Therapeutic significance:

NR4A3's link to Ewing sarcoma, a highly malignant tumor affecting children and adolescents, highlights its therapeutic potential. The protein's role in disease pathogenesis, driven by chromosomal aberrations, opens avenues for targeted therapies. Understanding NR4A3's function could lead to innovative treatments for Ewing sarcoma and possibly other NR4A3-related conditions.

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