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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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 employ our advanced, specialised process to create targeted 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 distinguishes itself through several key aspects:


  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.

  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.

  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.

  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q15782

UPID:
CH3L2_HUMAN

ALTERNATIVE NAMES:
Chondrocyte protein 39; YKL-39

ALTERNATIVE UPACC:
Q15782; A6NNY3; B4DPR7; Q15749; Q15783; Q5VUV7; Q96F97

BACKGROUND:
The protein known as Chitinase-3-like protein 2, with alternative names Chondrocyte protein 39 and YKL-39, stands out for its ability to bind chitooligosaccharides and various glycans with high affinity. This lectin's specificity for glycans, excluding heparin, and its absence of chitinase activity, highlight its unique biological role.

THERAPEUTIC SIGNIFICANCE:
Exploring the function of Chitinase-3-like protein 2 offers a promising pathway to novel therapeutic approaches. Its distinct glycan-binding capability points to a critical role in biological systems, potentially leading to innovative treatments.

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.