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.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


We utilise our cutting-edge, exclusive workflow to develop focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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
Q9H2F3

UPID:
3BHS7_HUMAN

ALTERNATIVE NAMES:
3 beta-hydroxysteroid dehydrogenase type VII; 3-beta-hydroxy-Delta(5)-C27 steroid oxidoreductase; Cholest-5-ene-3-beta,7-alpha-diol 3-beta-dehydrogenase

ALTERNATIVE UPACC:
Q9H2F3; Q96M28; Q9BSN9

BACKGROUND:
3 beta-hydroxysteroid dehydrogenase type 7, also known as Cholest-5-ene-3-beta,7-alpha-diol 3-beta-dehydrogenase, is integral to the production of hormonal steroids and bile acids. Its involvement in the degradation of 7-alpha,25-dihydroxycholesterol underlines its importance in cell positioning and movement in lymphoid tissues.

THERAPEUTIC SIGNIFICANCE:
Given its critical role in bile acid synthesis, mutations affecting 3β-HSD7 are responsible for Congenital bile acid synthesis defect 1, leading to severe liver conditions. Targeting this enzyme's pathway offers a promising avenue for developing treatments for related liver diseases.

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