AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for ADP-ribosylation factor-like protein 4D

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 utilise our cutting-edge, exclusive workflow to develop 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.

partner

Reaxense

upacc

P49703

UPID:

ARL4D_HUMAN

Alternative names:

ADP-ribosylation factor-like protein 4L

Alternative UPACC:

P49703; B2RC59; D3DX43

Background:

ADP-ribosylation factor-like protein 4D, alternatively known as ADP-ribosylation factor-like protein 4L, plays a crucial role in cellular processes by cycling between inactive GDP-bound and active GTP-bound forms. This cycling is regulated by guanine nucleotide exchange factors (GEF) and GTPase-activating proteins (GAP). It uniquely does not activate the cholera toxin catalytic subunit but recruits CYTH1, CYTH2, CYTH3, and CYTH4 to the plasma membrane in its GDP-bound form.

Therapeutic significance:

Understanding the role of ADP-ribosylation factor-like protein 4D could open doors to potential therapeutic strategies.

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