Focused On-demand Library for Alpha-ketoglutarate-dependent dioxygenase FTO

Available from Reaxense
Predicted by Alphafold

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.

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 methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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.







Alternative names:

Fat mass and obesity-associated protein; U6 small nuclear RNA (2'-O-methyladenosine-N(6)-)-demethylase FTO; U6 small nuclear RNA N(6)-methyladenosine-demethylase FTO; mRNA (2'-O-methyladenosine-N(6)-)-demethylase FTO; mRNA N(6)-methyladenosine demethylase FTO; tRNA N1-methyl adenine demethylase FTO

Alternative UPACC:

Q9C0B1; A2RUH1; B2RNS0; Q0P676; Q7Z785


Alpha-ketoglutarate-dependent dioxygenase FTO, known for its roles in RNA demethylation and energy homeostasis, is pivotal in regulating fat mass and adipogenesis. It specifically targets N(6)-methyladenosine (m6A) in various RNA species, influencing mRNA expression and stability. This protein also plays a crucial role in the differentiation of adipocytes into brown or white fat cells, affecting body size and fat accumulation.

Therapeutic significance:

FTO's involvement in severe polymalformation syndrome and obesity highlights its potential as a therapeutic target. Understanding the role of Alpha-ketoglutarate-dependent dioxygenase FTO could open doors to potential therapeutic strategies for these conditions, especially given its regulatory role in fat mass and energy homeostasis.

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