AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Sigma non-opioid intracellular receptor 1

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.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner 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 use our state-of-the-art dedicated workflow for designing focused libraries for receptors.

 Fig. 1. The sreening workflow of Receptor.AI

It features thorough molecular simulations of the receptor within its native membrane environment, complemented by ensemble virtual screening that considers its conformational mobility. For dimeric or oligomeric receptors, the full functional complex is constructed, and tentative binding sites are determined on and between the subunits to cover the entire spectrum of potential mechanisms of action.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

partner

Reaxense

upacc

Q99720

UPID:

SGMR1_HUMAN

Alternative names:

Aging-associated gene 8 protein; SR31747-binding protein; Sigma 1-type opioid receptor

Alternative UPACC:

Q99720; D3DRM7; O00673; O00725; Q0Z9W6; Q153Z1; Q2TSD1; Q53GN2; Q7Z653; Q8N7H3; Q9NYX0

Background:

Sigma non-opioid intracellular receptor 1, also known as Aging-associated gene 8 protein or Sigma 1-type opioid receptor, plays a pivotal role in lipid transport, receptor regulation, and cellular signaling. Its involvement in BDNF and EGF signaling, ion channel modulation, and calcium signaling underscores its multifaceted role in cell functions including proliferation, survival, and death. Additionally, it is crucial for mitochondrial axonal transport in motor neurons and protects against oxidative stress-induced cell death.

Therapeutic significance:

Linked to Amyotrophic lateral sclerosis 16 and Distal spinal muscular atrophy, autosomal recessive, 2, Sigma non-opioid intracellular receptor 1's genetic variants highlight its potential in neurodegenerative disease research. Understanding its role could open doors to novel therapeutic strategies, particularly in targeting motor neuron diseases and enhancing neuroprotective mechanisms.

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