Focused On-demand Library for Otoferlin

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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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 use our state-of-the-art dedicated workflow for designing focused libraries.

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.







Alternative names:

Fer-1-like protein 2

Alternative UPACC:

Q9HC10; B4DJX0; B5MCC1; B9A0H6; Q53R90; Q9HC08; Q9HC09; Q9Y650


Otoferlin, encoded by the gene with accession number Q9HC10, is a pivotal calcium ion sensor critical for synaptic vesicle-plasma membrane fusion and neurotransmitter release in cochlear inner hair cells (IHCs). It interacts with presynaptic SNARE proteins in a calcium-dependent manner, facilitating exocytosis of neurotransmitters. Otoferlin's role extends to synaptic exocytosis in outer hair cells (OHCs) and possibly in the recycling of endosomes.

Therapeutic significance:

Otoferlin is directly implicated in two forms of sensorineural hearing loss: Deafness, autosomal recessive, 9, and Auditory neuropathy, autosomal recessive, 1. These conditions underscore the protein's critical role in auditory processing. Understanding the role of Otoferlin could open doors to potential therapeutic strategies for these hearing impairments.

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