AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Mitochondrial inner membrane protease ATP23 homolog

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 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.

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

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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

Q9Y6H3

UPID:

ATP23_HUMAN

Alternative names:

Ku70-binding protein 3; XRCC6-binding protein 1

Alternative UPACC:

Q9Y6H3; Q1RLM4; Q96E81

Background:

The Mitochondrial inner membrane protease ATP23 homolog, also known as Ku70-binding protein 3 and XRCC6-binding protein 1, plays a crucial role in mitochondrial maintenance. Its involvement in the processing and maturation of mitochondrial proteins underscores its importance in cellular energy metabolism and integrity.

Therapeutic significance:

Understanding the role of Mitochondrial inner membrane protease ATP23 homolog could open doors to potential therapeutic strategies. Its pivotal function in mitochondrial health suggests its potential in targeting diseases related to mitochondrial dysfunction.

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