AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Bone morphogenetic protein 6

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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.

Our high-tech, dedicated method is applied to construct targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.

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

Reaxense

upacc

P22004

UPID:

BMP6_HUMAN

Alternative names:

VG-1-related protein

Alternative UPACC:

P22004; Q5TCP3

Background:

Bone morphogenetic protein 6 (BMP6), also known as VG-1-related protein, is a pivotal growth factor within the TGF-beta superfamily. It orchestrates critical roles in developmental processes, notably in cartilage and bone formation. BMP6 is instrumental in iron metabolism regulation, acting as a ligand for hemojuvelin/HJV to modulate HAMP/hepcidin expression. It triggers the canonical BMP signaling cascade through interaction with receptors ACVR1 and ACVR2B, and engages in non-canonical pathways like the TAZ-Hippo signaling to influence VEGF signaling.

Therapeutic significance:

BMP6's involvement in iron overload, a disorder of iron homeostasis, underscores its therapeutic potential. Understanding BMP6's regulatory role in iron metabolism and its impact on diseases like iron overload could pave the way for innovative treatment strategies, leveraging its signaling pathways to correct iron imbalances.

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