AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Estrogen-related receptor gamma

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

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

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

Reaxense

upacc

P62508

UPID:

ERR3_HUMAN

Alternative names:

ERR gamma-2; Estrogen receptor-related protein 3; Nuclear receptor subfamily 3 group B member 3

Alternative UPACC:

P62508; A8K4I0; A8K6I2; B3KY84; E9PGB7; F8W8J3; O75454; O96021; Q68DA0; Q6P274; Q6PK28; Q6TS38; Q9R1F3; Q9UNJ4

Background:

The Estrogen-related receptor gamma, known alternatively as ERR gamma-2, Estrogen receptor-related protein 3, or Nuclear receptor subfamily 3 group B member 3, is a pivotal orphan receptor. It functions as a transcription activator even without a bound ligand, specifically binding to an estrogen response element to activate reporter genes. This protein plays a crucial role in inducing the expression of PERM1 in skeletal muscle, highlighting its significance in muscle physiology.

Therapeutic significance:

Understanding the role of Estrogen-related receptor gamma could open doors to potential therapeutic strategies, particularly in enhancing muscle function and addressing metabolic disorders.

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