AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Acireductone dioxygenase

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.

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.

Our top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across 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

Q9BV57

UPID:

MTND_HUMAN

Alternative names:

Acireductone dioxygenase (Fe(2+)-requiring); Acireductone dioxygenase (Ni(2+)-requiring); Membrane-type 1 matrix metalloproteinase cytoplasmic tail-binding protein 1; Submergence-induced protein-like factor

Alternative UPACC:

Q9BV57; D6W4Y3; Q53HW3; Q53QD3; Q57YV7; Q68CK2; Q6ZSF7; Q7Z512; Q96P85; Q9NV57

Background:

Acireductone dioxygenase, with alternative names such as Acireductone dioxygenase (Fe(2+)-requiring) and (Ni(2+)-requiring), plays a pivotal role in the methionine recycle pathway. It catalyzes reactions involving oxygen and acireductone, producing different compounds based on the metal present in its active site. The enzyme's versatility extends to down-regulating cell migration mediated by MMP14 and facilitating hepatitis C virus replication in specific cell lines.

Therapeutic significance:

Understanding the role of Acireductone dioxygenase could open doors to potential therapeutic strategies, especially considering its involvement in essential metabolic pathways and virus replication mechanisms.

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