AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for RILP-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.

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 employ our advanced, specialised process to create 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.

partner

Reaxense

upacc

Q5EBL4

UPID:

RIPL1_HUMAN

Alternative names:

Rab-interacting lysosomal-like protein 1

Alternative UPACC:

Q5EBL4; Q66K36; Q8N1M0

Background:

RILP-like protein 1, alternatively known as Rab-interacting lysosomal-like protein 1, is pivotal in cell shape regulation and polarity. It plays a crucial role in cellular protein transport, including away from primary cilia, and acts as a neuroprotective agent by sequestering GAPDH in the cytosol. This protein is also involved in the inhibition of ciliogenesis through its interaction with RAB10 following LRRK2-mediated phosphorylation.

Therapeutic significance:

RILP-like protein 1's association with Oculopharyngodistal myopathy 4, a muscle disorder characterized by progressive muscle weakness, highlights its therapeutic potential. Understanding the role of RILP-like protein 1 could open doors to potential therapeutic strategies for treating this debilitating condition.

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