Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by Reaxense.


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

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
Q9UNE0

UPID:
EDAR_HUMAN

ALTERNATIVE NAMES:
Anhidrotic ectodysplasin receptor 1; Downless homolog; EDA-A1 receptor; Ectodermal dysplasia receptor; Ectodysplasin-A receptor

ALTERNATIVE UPACC:
Q9UNE0; B2R9H2; B4DLC5; D3DX74; E9PC98; Q52LL5; Q9UND9

BACKGROUND:
The Ectodysplasin-A receptor, encoded by the EDAR gene, is integral to ectodermal structure development. It mediates critical signaling pathways, including NF-kappa-B and JNK, essential for hair, teeth, and sweat gland formation. Its alternative names include EDA-A1 receptor and Ectodermal dysplasia receptor.

THERAPEUTIC SIGNIFICANCE:
Mutations in EDAR result in ectodermal dysplasia types 10A and 10B, characterized by hypotrichosis, abnormal teeth, and hypohidrosis. Exploring the function of EDAR offers a promising avenue for developing novel therapies for these genetic disorders.

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.