Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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.


We employ our advanced, specialised process to create targeted libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse 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
P56470

UPID:
LEG4_HUMAN

ALTERNATIVE NAMES:
Antigen NY-CO-27; L-36 lactose-binding protein; Lactose-binding lectin 4

ALTERNATIVE UPACC:
P56470

BACKGROUND:
Galectin-4, with its alternative identities such as Antigen NY-CO-27 and Lactose-binding lectin 4, is a pivotal protein in the biological landscape. It binds a spectrum of sugars including lactose, highlighting its role in sugar recognition and binding. The protein's potential involvement in forming adherens junctions underscores its importance in cellular cohesion and communication, critical for tissue structure and function.

THERAPEUTIC SIGNIFICANCE:
The exploration of Galectin-4's functionalities offers a promising avenue for therapeutic innovation. Given its crucial role in the formation of adherens junctions, understanding this protein's mechanisms could pave the way for breakthroughs in treating diseases where cellular cohesion and communication are disrupted. Galectin-4 stands as a beacon of hope in the quest for novel therapeutic strategies.

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