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Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

Opioid Analgesics: Synthetic and Semisynthetic Opioids

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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: Overview01:22

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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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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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相关实验视频

Updated: Jun 27, 2025

Author Spotlight: An Efficient Methodology to Confidently Differentiate and Characterize Fentanyl Analogs
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使用基于图形的变压器模型探索新型芬太尼类比物.

Guangle Zhang1, Yuan Zhang2, Ling Li3

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

Interdisciplinary sciences, computational life sciences
|April 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种基于分子图的新型变压器模型,用于产生新的芬太尼类似物. 该模型成功地创造了许多新型化合物,有助于探索芬太尼分子结构和分布.

关键词:
深度生成模型深度生成模型芬太尼的类似物 芬太尼的类似物分子图谱 分子图表分子生成分子的产生.

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科学领域:

  • 计算化学计算化学
  • 药物发现 药物发现 药物发现
  • 人工智能的人工智能

背景情况:

  • 芬太尼及其类似物很容易被修改,由于数据库条目有限,这给监管监管带来了挑战.
  • 犯罪分子利用芬太尼类似物的结构变化来逃避检测.

研究的目的:

  • 开发一种新的分子生成模型,用于制造新的芬太尼类 analogue.
  • 为了加强芬太尼分子结构的探索,并了解它们的分布模式.

主要方法:

  • 采用了基于分子图的变压器模型.
  • 集成了一个使用子结构替换的数据增强技术.
  • 生成的分子被选,并计算了它们的特性.

主要成果:

  • 产生了14万种潜在的芬太尼类似物,在查后发现了36799种.
  • 该模型展示了学习原始芬太尼分子特性的能力.
  • 与其他深度学习模型相比,拟议的模型产生了更多的新型芬太尼类型.

结论:

  • 基于分子图的变压器模型有效地产生了新的芬太尼类型.
  • 这种方法有助于探索潜在芬太尼分子的结构格局.
  • 这些发现有助于了解芬太尼类似物的分布.