AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for S-adenosylmethionine sensor upstream of mTORC1

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 high-tech, dedicated method is applied to construct targeted libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

Q1RMZ1

UPID:

SAMTR_HUMAN

Alternative names:

Probable methyltransferase BMT2 homolog

Alternative UPACC:

Q1RMZ1; Q8N3D0; Q96MV7

Background:

The S-adenosylmethionine sensor upstream of mTORC1, also known as Probable methyltransferase BMT2 homolog, is a pivotal S-adenosyl-L-methionine-binding protein. It regulates mTORC1 signaling through interactions with the GATOR1 and KICSTOR complexes, responding to methionine levels. This protein serves as a methionine sufficiency sensor, promoting or inhibiting mTORC1 signaling based on S-adenosyl-L-methionine availability.

Therapeutic significance:

Understanding the role of S-adenosylmethionine sensor upstream of mTORC1 could open doors to potential therapeutic strategies.

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