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.


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
P02818

UPID:
OSTCN_HUMAN

ALTERNATIVE NAMES:
Bone Gla protein; Gamma-carboxyglutamic acid-containing protein

ALTERNATIVE UPACC:
P02818; Q5TCK6

BACKGROUND:
Osteocalcin, identified as Bone Gla protein, acts as a negative regulator of bone formation, binding strongly to apatite and calcium. Beyond its structural role, the uncarboxylated form functions as a hormone, regulating energy metabolism, insulin secretion, testosterone production, and brain development, showcasing its multifaceted biological significance.

THERAPEUTIC SIGNIFICANCE:
Exploring Osteocalcin's hormonal functions offers a promising avenue for therapeutic intervention. Its impact on energy metabolism, testosterone synthesis, and neuronal survival suggests potential applications in managing diabetes, enhancing male fertility, and preventing cognitive decline.

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