AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Lithostathine-1-alpha

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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 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.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

P05451

UPID:

REG1A_HUMAN

Alternative names:

Islet cells regeneration factor; Islet of Langerhans regenerating protein; Pancreatic stone protein; Pancreatic thread protein; Regenerating islet-derived protein 1-alpha; Regenerating protein I alpha

Alternative UPACC:

P05451; P11379; Q4ZG28

Background:

Lithostathine-1-alpha, known by various names such as Islet cells regeneration factor and Pancreatic stone protein, plays a crucial role in biological systems. It acts as an inhibitor of spontaneous calcium carbonate precipitation and is linked to neuronal sprouting in the brain, as well as regeneration in the brain and pancreas.

Therapeutic significance:

Understanding the role of Lithostathine-1-alpha could open doors to potential therapeutic strategies, particularly in the context of pancreatic and neurological disorders.

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