Focused On-demand Library for Steroid hormone receptor ERR2

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 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.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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 screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast 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
O95718

UPID:
ERR2_HUMAN

ALTERNATIVE NAMES:
ERR beta-2; Estrogen receptor-like 2; Estrogen-related receptor beta; Nuclear receptor subfamily 3 group B member 2

ALTERNATIVE UPACC:
O95718; A2VDJ2; B6ZGU4; Q5F0P7; Q5F0P8; Q9HCB4

BACKGROUND:
ERR2, recognized for its binding to the ESRRB recognition sequence, acts as a transcription factor influencing embryonic and trophoblast stem cell fate. It modulates the expression of genes critical for rod cell survival and pluripotency maintenance, engaging in various signaling pathways.

THERAPEUTIC SIGNIFICANCE:
Given its association with deafness, autosomal recessive, 35, ERR2's genetic underpinnings offer a promising avenue for therapeutic exploration. Delving into Steroid hormone receptor ERR2's function could unveil novel treatment pathways.

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