AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for ADP-ribosylation factor-like protein 2-binding protein

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.

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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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

Q9Y2Y0

UPID:

AR2BP_HUMAN

Alternative names:

Binder of ARF2 protein 1

Alternative UPACC:

Q9Y2Y0; B3KQJ5; Q504R0

Background:

ADP-ribosylation factor-like protein 2-binding protein, also known as Binder of ARF2 protein 1, is pivotal in cellular processes, including the nuclear translocation and retention of STAT3. Its collaboration with ARL2 underscores its essential role in transcriptional activities, influencing gene expression and cellular responses.

Therapeutic significance:

The protein's association with Retinitis pigmentosa 82 with or without situs inversus, a disorder impacting vision and organ positioning, highlights its clinical relevance. Understanding the role of ADP-ribosylation factor-like protein 2-binding protein could open doors to potential therapeutic strategies for this genetic condition.

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