AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Brain-specific angiogenesis inhibitor 1-associated protein 2

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.

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.

Our top-notch dedicated system is used to design specialised 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.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

Q9UQB8

UPID:

BAIP2_HUMAN

Alternative names:

Fas ligand-associated factor 3; Insulin receptor substrate p53/p58; Insulin receptor substrate protein of 53 kDa

Alternative UPACC:

Q9UQB8; O43858; Q53HB1; Q86WC1; Q8N5C0; Q96CR7; Q9UBR3; Q9UQ43

Background:

Brain-specific angiogenesis inhibitor 1-associated protein 2, also known as Fas ligand-associated factor 3, plays a pivotal role in cellular processes by linking membrane-bound small G-proteins to cytoplasmic effector proteins. It is essential for CDC42-mediated actin cytoskeleton reorganization and RAC1-mediated membrane ruffling. This protein also contributes to neurite growth, filopodia formation, and actin cytoskeleton reorganization in response to bacterial infection.

Therapeutic significance:

Understanding the role of Brain-specific angiogenesis inhibitor 1-associated protein 2 could open doors to potential therapeutic strategies.

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