AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Rapamycin-insensitive companion of mTOR

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.

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.

Our top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of 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

Q6R327

UPID:

RICTR_HUMAN

Alternative names:

AVO3 homolog

Alternative UPACC:

Q6R327; B2RNX0; B7ZMF7; Q68DT5; Q86UB7; Q8N3A0; Q8NCM6

Background:

The Rapamycin-insensitive companion of mTOR, also known as AVO3 homolog, is a pivotal subunit of mTORC2. This complex plays a crucial role in regulating cell growth and survival in response to hormonal signals. Unlike mTORC1, mTORC2's activity is not influenced by nutrients, positioning it as a key player in the regulation of the actin cytoskeleton through Rho GTPases and the phosphorylation of AKT1, SGK1, and PRKCA. Its involvement in embryonic growth and development underscores its biological significance.

Therapeutic significance:

Understanding the role of the Rapamycin-insensitive companion of mTOR could open doors to potential therapeutic strategies.

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