AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Putative cytochrome b-c1 complex subunit Rieske-like protein 1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of 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

P0C7P4

UPID:

UCRIL_HUMAN

Alternative names:

Ubiquinol-cytochrome c reductase Rieske iron-sulfur subunit pseudogene 1

Alternative UPACC:

P0C7P4

Background:

The Putative cytochrome b-c1 complex subunit Rieske-like protein 1, also known as Ubiquinol-cytochrome c reductase Rieske iron-sulfur subunit pseudogene 1, plays a crucial role in the mitochondrial electron transport chain. This protein is pivotal for the efficient production of ATP through oxidative phosphorylation, a fundamental process for cellular energy supply.

Therapeutic significance:

Understanding the role of Putative cytochrome b-c1 complex subunit Rieske-like protein 1 could open doors to potential therapeutic strategies. Its involvement in the electron transport chain suggests its potential impact on metabolic disorders and mitochondrial diseases.

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