Focused On-demand Libraries - Receptor.AI 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. 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.


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


 

Fig. 1. The screening 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 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
Q9HBW1

UPID:
LRRC4_HUMAN

ALTERNATIVE NAMES:
Brain tumor-associated protein BAG; Nasopharyngeal carcinoma-associated gene 14 protein; Netrin-G2 ligand

ALTERNATIVE UPACC:
Q9HBW1; A4D0Y9; Q14DU9; Q6ZMI8; Q96A85

BACKGROUND:
The Leucine-rich repeat-containing protein 4, with alternative names such as Brain tumor-associated protein BAG and Netrin-G2 ligand, is key in synaptic adhesion and excitatory synapse formation. It organizes dendritic differentiation specific to lamina/pathway and is vital for auditory responses. Its role in glioma suppression suggests its importance in neural health and potential in disease treatment.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Leucine-rich repeat-containing protein 4 offers promising avenues for developing novel therapeutic approaches.

Looking for more information on this library or underlying technology? Fill out the form below and we will be in touch with all the details you need.