Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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 utilise our cutting-edge, exclusive workflow to develop 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 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
Q86VD1

UPID:
MORC1_HUMAN

ALTERNATIVE NAMES:
Cancer/testis antigen 33

ALTERNATIVE UPACC:
Q86VD1; B4DYX1; E7ERX1; Q7L8E2; Q9NSG7; Q9Y6D4

BACKGROUND:
The protein known as MORC family CW-type zinc finger protein 1, or alternatively Cancer/testis antigen 33, is essential for spermatogenesis. By facilitating de novo DNA methylation and the silencing of transposable elements within male embryonic germ cells, it plays a critical role in maintaining genomic integrity and ensuring the proper development of germ cells.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of MORC family CW-type zinc finger protein 1 offers a promising avenue for developing novel therapeutic approaches. Given its crucial role in germ cell development and genomic stability, targeting this protein could lead to breakthroughs in treating infertility and diseases associated with genomic instability, such as certain cancers.

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