Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 Reaxense.


The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We employ our advanced, specialised process to create targeted 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
O76003

UPID:
GLRX3_HUMAN

ALTERNATIVE NAMES:
PKC-interacting cousin of thioredoxin; PKC-theta-interacting protein; Thioredoxin-like protein 2

ALTERNATIVE UPACC:
O76003; B3KMP7; B3KMQ5; D3DRG2; Q5JV01; Q96CE0; Q9P1B0; Q9P1B1

BACKGROUND:
Glutaredoxin-3, identified by its alternative names such as PKC-interacting cousin of thioredoxin, plays a critical role in the assembly of cytosolic iron-sulfur clusters, facilitating the insertion of these clusters into a subset of proteins. Its involvement extends to acting as a negative regulator of cardiac hypertrophy and aiding in hemoglobin maturation, showcasing its importance in cardiovascular and hematological processes.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Glutaredoxin-3 offers a promising avenue for developing novel therapeutic approaches, especially in diseases related to heart enlargement and anemia.

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