Focused On-demand Library for Acireductone dioxygenase

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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.

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

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.