Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


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 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
Q9P0K1

UPID:
ADA22_HUMAN

ALTERNATIVE NAMES:
Metalloproteinase-disintegrin ADAM22-3; Metalloproteinase-like, disintegrin-like, and cysteine-rich protein 2

ALTERNATIVE UPACC:
Q9P0K1; O75075; O75076; Q9P0K2; Q9UIA1; Q9UKK2

BACKGROUND:
The protein ADAM22, known for its non-catalytic metalloprotease-like activity, is crucial in brain function, particularly in cell adhesion and anti-proliferative processes. Its role as a receptor for LGI1 highlights its significance in neuronal health and disease mechanisms.

THERAPEUTIC SIGNIFICANCE:
Given ADAM22's critical role in the pathogenesis of Developmental and Epileptic Encephalopathy 61, a disorder marked by severe epilepsy and cognitive delays, targeting this protein could open new therapeutic avenues. Exploring ADAM22's mechanisms offers a promising path for developing targeted treatments.

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