Focused On-demand Library for Mu-type opioid receptor

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.

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 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 top-notch dedicated system is used to design specialised libraries for receptors.

 Fig. 1. The sreening workflow of Receptor.AI

It includes extensive molecular simulations of the receptor in its native membrane environment and the ensemble virtual screening accounting for its conformational mobility. In the case of dimeric or oligomeric receptors, the whole functional complex is modelled, and the tentative binding pockets are determined on and between the subunits to cover the whole spectrum of possible mechanisms of action.

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.







Alternative names:

Mu opiate receptor; Mu opioid receptor

Alternative UPACC:

P35372; B0FXJ1; B2R9S7; B8Q1L7; B8Q1L8; B8Q1L9; E7EWZ3; G8XRH6; G8XRH8; Q12930; Q4VWM1; Q4VWM2; Q4VWM3; Q4VWM4; Q4VWM6; Q4VWX6; Q5TDA1; Q6UPP1; Q6UQ80; Q7Z2D8; Q86V80; Q8IWW3; Q8IWW4; Q9UCZ4; Q9UN57


The Mu-type opioid receptor, encoded by the gene P35372, plays a pivotal role in mediating the effects of opioids, including natural endogenous peptides and synthetic drugs. It functions as a receptor for substances like beta-endorphin and various opioids such as morphine and fentanyl, triggering a cascade of intracellular signaling pathways. This receptor's activity involves coupling with G-proteins to modulate cellular responses, including the inhibition of adenylate cyclase and regulation of ion channels and MAPK pathways.

Therapeutic significance:

Understanding the role of the Mu-type opioid receptor could open doors to potential therapeutic strategies. Its involvement in pain modulation and reward systems makes it a critical target for treating pain and addiction. The receptor's complex signaling mechanisms offer multiple intervention points for developing drugs with improved efficacy and reduced side effects.

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