AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Acyl-coenzyme A thioesterase 1

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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

Our high-tech, dedicated method is applied to construct targeted 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 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

Q86TX2

UPID:

ACOT1_HUMAN

Alternative names:

CTE-I; CTE-Ib; Inducible cytosolic acyl-coenzyme A thioester hydrolase; Long chain acyl-CoA thioester hydrolase; Palmitoyl-coenzyme A thioesterase

Alternative UPACC:

Q86TX2; A1L173; Q3I5F9

Background:

Acyl-coenzyme A thioesterase 1, known by alternative names such as CTE-I and Long chain acyl-CoA thioesterase, plays a crucial role in lipid metabolism. It catalyzes the hydrolysis of acyl-CoAs to free fatty acids and coenzyme A, particularly acting on long chain fatty acyl-CoAs (C12-C20). This enzymatic activity is vital for regulating intracellular levels of these molecules.

Therapeutic significance:

Understanding the role of Acyl-coenzyme A thioesterase 1 could open doors to potential therapeutic strategies. Its involvement in lipid metabolism suggests its potential impact on metabolic disorders, offering a promising avenue for research and drug development.

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