AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Choriogonadotropin subunit beta 3

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, 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.

Our high-tech, dedicated method is applied to construct targeted libraries.

 Fig. 1. The sreening 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

Reaxense

upacc

P0DN86

UPID:

CGB3_HUMAN

Alternative names:

Choriogonadotropin subunit beta; Chorionic gonadotropin chain beta

Alternative UPACC:

P0DN86; A1A5E0; B9ZVP5; P01233; Q13991; Q14000; Q3KPI3; Q3SY41; Q8WTT5; Q8WXL1; Q8WXL2; Q8WXL3; Q8WXL4

Background:

Choriogonadotropin subunit beta 3, also known as Choriogonadotropin subunit beta and Chorionic gonadotropin chain beta, plays a pivotal role in pregnancy. It is part of the human chorionic gonadotropin (hCG), a complex glycoprotein with two glycosylated subunits, alpha and beta, which are non-covalently associated. The alpha subunit is common across pituitary gonadotropin hormones, while the beta subunits provide specificity. This protein is crucial for stimulating the ovaries to synthesize steroids, vital for maintaining pregnancy.

Therapeutic significance:

Understanding the role of Choriogonadotropin subunit beta 3 could open doors to potential therapeutic strategies.

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