Focused On-demand Library for Battenin

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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated 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 high-tech, dedicated method is applied to construct targeted 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.

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:

Batten disease protein; Protein CLN3

Alternative UPACC:

Q13286; B2R7J1; B4DXL3; O00668; O95089; Q549S9; Q9UP09; Q9UP11; Q9UP12; Q9UP13; Q9UP14


Battenin, also known as the Batten disease protein or Protein CLN3, plays a crucial role in microtubule-dependent transport processes. It connects the Golgi network, endosomes, autophagosomes, lysosomes, and the plasma membrane, influencing cellular processes such as lysosomal pH regulation, protein degradation, and synaptic transmission. Its interaction with various cellular components modulates critical functions including autophagy, apoptosis, and cell migration.

Therapeutic significance:

Battenin's involvement in Ceroid lipofuscinosis, neuronal, 3, a neurodegenerative disorder characterized by seizures, dementia, and visual loss, underscores its therapeutic potential. Understanding Battenin's function could lead to novel therapeutic strategies for managing this condition, highlighting the importance of research in this area.

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