Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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 use our state-of-the-art dedicated workflow for designing focused libraries.


 

Fig. 1. The screening 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.


Our library is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
Q9ULK5

UPID:
VANG2_HUMAN

ALTERNATIVE NAMES:
Loop-tail protein 1 homolog; Strabismus 1; Van Gogh-like protein 2

ALTERNATIVE UPACC:
Q9ULK5; D3DVE9; Q5T212

BACKGROUND:
Vang-like protein 2, identified by its alternative names Loop-tail protein 1 homolog, Strabismus 1, and Van Gogh-like protein 2, is essential for early developmental processes, including axial midline structure formation and neural plate development. It regulates planar cell polarity and is necessary for the correct orientation of cochlear stereociliary bundles. Additionally, it facilitates the cell surface localization of FZD3 and FZD6 in the inner ear.

THERAPEUTIC SIGNIFICANCE:
The association of Vang-like protein 2 with neural tube defects, which are caused by improper neural tube closure and have multifactorial etiology, highlights its potential as a therapeutic target. Understanding the role of Vang-like protein 2 could open doors to potential therapeutic strategies.

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