Focused On-demand Library for Protein arginine N-methyltransferase 9

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.

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

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We use our state-of-the-art dedicated workflow for designing focused libraries.

Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.

Key features that set our library apart include:

The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q6P2P2

UPID:

ANM9_HUMAN

Alternative names:

Protein arginine N-methyltransferase 10

Alternative UPACC:

Q6P2P2; A8KA39; B3KU92; Q6ZR58; Q8N383; Q9BT55; Q9NT98

Background:

Protein arginine N-methyltransferase 9 (PRMT9), also known as Protein arginine N-methyltransferase 10, plays a crucial role in post-translational modifications. It is adept at catalyzing the formation of omega-N monomethylarginine (MMA) and symmetrical dimethylarginine (sDMA), with a specific action on the symmetrical dimethylation of SF3B2. This enzyme is pivotal in the regulation of alternative splicing of pre-mRNA, influencing gene expression and cellular function.

Therapeutic significance:

Understanding the role of Protein arginine N-methyltransferase 9 could open doors to potential therapeutic strategies. Its involvement in post-translational modifications and regulation of gene expression highlights its potential as a target for drug discovery, aiming to modulate its activity for therapeutic benefits.