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Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

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Synthetic and semisynthetic opioids are pivotal in pain management and tackling opioid addiction. Semisynthetic opioids, including morphinans (morphine derivatives), oxycodone, oxymorphone, hydrocodone, and hydromorphone, have improved pharmacokinetic profiles compared to morphine. Additionally, heroin and 6-MAM (6-Monoacetylmorphine) show better CNS penetration than morphine due to heightened lipid solubility. Hydromorphone, a potent opioid, undergoes hepatic metabolism to form the active...
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Opioid receptors, including the mu (μ, MOR), delta (δ, DOR), and kappa (κ, KOR) types, belong to the rhodopsin family of G protein-coupled receptors. These receptors are located throughout the central and peripheral nervous systems and in non-neuronal tissues such as macrophages and astrocytes. Opioid receptor ligands can be categorized into agonists or antagonists. Highly selective agonists include [d-Ala2, MePhe4, Gly(ol)5]-enkephalin or DAMGO for MOR, [D-Pen2,...
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Opioids are a class of drugs that mimic endogenous opioid peptides and act on opioid receptors, and help in pain relief. These compounds are classified as natural, synthetic, or semi-synthetic. Natural opioids, like morphine, codeine, and thebaine, are derived from the opium poppy plant (Papaver somniferum or Papaver album) and are termed opiates. Synthetic opioids are artificial, while semi-synthetic opioids combine natural and synthetic compounds. Morphine, a prototypical opioid, possesses a...
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Related Experiment Video

Updated: Jun 27, 2025

Author Spotlight: An Efficient Methodology to Confidently Differentiate and Characterize Fentanyl Analogs
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Exploring Novel Fentanyl Analogues Using a Graph-Based Transformer Model.

Guangle Zhang1, Yuan Zhang2, Ling Li3

  • 1College of Science, Wuxi University, 214105, Wuxi, China.

Interdisciplinary Sciences, Computational Life Sciences
|April 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel molecular graph-based transformer model to generate new fentanyl analogues. The model successfully created numerous novel compounds, aiding in the exploration of fentanyl molecule structures and distributions.

Keywords:
Deep generative modelFentanyl analoguesMolecular graphMolecule generation

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Artificial Intelligence

Background:

  • Fentanyl and its analogues are easily modified, posing challenges for regulatory supervision due to limited database entries.
  • Criminals exploit the structural variability of fentanyl analogues to evade detection.

Purpose of the Study:

  • To develop a novel molecular generation model for creating new fentanyl analogues.
  • To enhance the exploration of fentanyl molecule structures and understand their distribution patterns.

Main Methods:

  • A molecular graph-based transformer model was employed.
  • A data augmentation technique using substructure replacement was integrated.
  • Generated molecules were screened, and their properties were calculated.

Main Results:

  • 140,000 potential fentanyl analogues were generated, with 36,799 identified after screening.
  • The model demonstrated an ability to learn properties of original fentanyl molecules.
  • The proposed model generated more novel fentanyl analogues compared to other deep learning models.

Conclusions:

  • The molecular graph-based transformer model effectively generates novel fentanyl analogues.
  • This approach aids in exploring the structural landscape of potential fentanyl molecules.
  • The findings contribute to understanding the distribution of fentanyl analogues.