AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Elongation factor Tu, mitochondrial

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.

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 employ our advanced, specialised process to create targeted libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.

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.

partner

Reaxense

upacc

P49411

UPID:

EFTU_HUMAN

Alternative names:

P43

Alternative UPACC:

P49411; O15276

Background:

Elongation factor Tu, mitochondrial (P49411), also known as P43, plays a pivotal role in protein biosynthesis by promoting the GTP-dependent binding of aminoacyl-tRNA to the ribosome's A-site. Beyond its fundamental role in translation, it regulates autophagy and innate immunity, coordinating the recruitment of ATG5-ATG12 and NLRX1 at mitochondria. This action serves as a critical checkpoint in the RIGI-MAVS pathway, balancing the inhibition of RLR-mediated type I interferon production with the promotion of autophagy.

Therapeutic significance:

P43's involvement in Combined oxidative phosphorylation deficiency 4, a mitochondrial disease characterized by neonatal lactic acidosis and severely decreased mitochondrial protein synthesis, underscores its therapeutic potential. Understanding the role of Elongation factor Tu, mitochondrial could open doors to potential therapeutic strategies for mitochondrial diseases.

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.