AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for S-methylmethionine--homocysteine S-methyltransferase BHMT2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced activity, selectivity, and safety.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner Reaxense.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

We utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.

partner

Reaxense

upacc

Q9H2M3

UPID:

BHMT2_HUMAN

Alternative names:

Betaine--homocysteine S-methyltransferase 2

Alternative UPACC:

Q9H2M3; B7Z516; Q9NXX7

Background:

S-methylmethionine--homocysteine S-methyltransferase BHMT2, also known as Betaine--homocysteine S-methyltransferase 2, plays a crucial role in homocysteine metabolism by converting homocysteine to methionine using S-methylmethionine (SMM) as a methyl donor. This process is vital for maintaining methionine levels and ensuring proper cellular function.

Therapeutic significance:

Understanding the role of S-methylmethionine--homocysteine S-methyltransferase BHMT2 could open doors to potential therapeutic strategies. Its involvement in homocysteine metabolism suggests a possible link to conditions related to methionine dysregulation, offering a promising avenue for research into novel treatments.

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