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.


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.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q96NY8

UPID:
NECT4_HUMAN

ALTERNATIVE NAMES:
Ig superfamily receptor LNIR; Nectin cell adhesion molecule 4; Poliovirus receptor-related protein 4

ALTERNATIVE UPACC:
Q96NY8; B4DQW3; Q96K15

BACKGROUND:
The protein Nectin-4, alternatively named Ig superfamily receptor LNIR, Nectin cell adhesion molecule 4, and Poliovirus receptor-related protein 4, is implicated in cell adhesion mechanisms and acts as a receptor for measles virus. Its interactions are crucial for both trans-homophilic and -heterophilic cell adhesion processes.

THERAPEUTIC SIGNIFICANCE:
Linked to Ectodermal dysplasia-syndactyly syndrome 1, Nectin-4's role in developmental disorders involving hair, teeth, and skin presents a unique opportunity for therapeutic exploration. The study of Nectin-4's function could lead to innovative treatments.

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