Focused On-demand Library for Protein arginine N-methyltransferase 7

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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 use our state-of-the-art dedicated workflow for designing focused 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 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:

Histone-arginine N-methyltransferase PRMT7; [Myelin basic protein]-arginine N-methyltransferase PRMT7

Alternative UPACC:

Q9NVM4; B3KPR0; B3KUG9; B4E379; Q96PV5; Q9H9L0


Protein arginine N-methyltransferase 7 (PRMT7) is known for its critical role in arginine methylation, impacting various biological processes. It catalyzes the formation of omega-N monomethylarginine and symmetrical dimethylarginine, with a preference for the former. PRMT7's activity is essential for the methylation of specific proteins, including histones and myelin basic protein, influencing snRNP core particle assembly, gene imprinting, and possibly embryonic stem cell pluripotency.

Therapeutic significance:

The involvement of PRMT7 in a rare autosomal recessive disease characterized by developmental delay, learning disabilities, and skeletal abnormalities highlights its potential as a therapeutic target. Understanding the role of PRMT7 could open doors to potential therapeutic strategies for treating this condition and possibly other related disorders.

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