Focused On-demand Library for Acyl-coenzyme A thioesterase MBLAC2

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 includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.

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.







Alternative names:

Beta-lactamase MBLAC2; Metallo-beta-lactamase domain-containing protein 2; Palmitoyl-coenzyme A thioesterase MBLAC2

Alternative UPACC:

Q68D91; D6RJI1; Q8IY16; Q8N8D8


Acyl-coenzyme A thioesterase MBLAC2, also known as Beta-lactamase MBLAC2 and Metallo-beta-lactamase domain-containing protein 2, plays a crucial role in cellular metabolism. It catalyzes the hydrolysis of acyl-CoAs to free fatty acids and CoASH, regulating levels of acyl-CoAs, free fatty acids, and CoASH. MBLAC2 shows specificity for long-chain fatty acyl-CoA thioesters, particularly palmitoyl-CoA, and exhibits beta-lactamase activity, breaking down penicillin G and nitrocefin.

Therapeutic significance:

Understanding the role of Acyl-coenzyme A thioesterase MBLAC2 could open doors to potential therapeutic strategies.

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