AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Myotubularin-related protein 13

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.

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.

Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.

Our top-notch dedicated system is used to design specialised 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

Q86WG5

UPID:

MTMRD_HUMAN

Alternative names:

Inactive phosphatidylinositol 3-phosphatase 13; SET-binding factor 2

Alternative UPACC:

Q86WG5; Q3MJF0; Q68DQ3; Q6P459; Q6PJD1; Q7Z325; Q7Z621; Q86VE2; Q96FE2; Q9C097

Background:

Myotubularin-related protein 13, also known as Inactive phosphatidylinositol 3-phosphatase 13 and SET-binding factor 2, plays a pivotal role in cellular processes. It functions as a Guanine nucleotide exchange factor (GEF), activating RAB21 and possibly RAB28, facilitating the conversion of GDP-bound Rab proteins into their active GTP-bound form. This protein is instrumental in starvation-induced autophagy, activating RAB21 for SNARE-mediated autophagosome-lysosome fusion, and acts as an adapter for the phosphatase MTMR2, enhancing its catalytic activity.

Therapeutic significance:

Myotubularin-related protein 13's involvement in Charcot-Marie-Tooth disease 4B2, a recessive demyelinating form, underscores its therapeutic significance. Understanding its role could lead to novel therapeutic strategies targeting the underlying genetic variants affecting this protein, offering hope for patients with this peripheral nervous system disorder.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.