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相关概念视频

Drug-Receptor Interaction: Agonist01:25

Drug-Receptor Interaction: Agonist

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Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
Agonists can bind to receptors in different ways. Some agonists bind directly to the receptor's active site, mimicking the endogenous...
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Chemotherapy-Induced Nausea and Vomiting: Cannabinoids01:21

Chemotherapy-Induced Nausea and Vomiting: Cannabinoids

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Tetrahydrocannabinol (THC) is a phytocannabinoid that primarily interacts with the CB1 receptor, a type of G protein-coupled receptor (GPCR) predominantly in and around the chemoreceptor trigger zone (CTZ) and emetic center. THC also blocks the serotonin receptor activity in the dorsal vagal complex (DVC) by inhibiting serotonin release. THC exerts its anti-emetic effects through these interactions, which are beneficial for patients undergoing chemotherapy.
Two synthetic agonists of THC,...
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Opioid Receptors: Overview01:22

Opioid Receptors: Overview

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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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The Two-State Receptor Model01:29

The Two-State Receptor Model

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
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Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
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CNS Stimulants: Cocaine, Amphetamines and Cannabinoids01:24

CNS Stimulants: Cocaine, Amphetamines and Cannabinoids

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CNS stimulants, such as cocaine, amphetamines, and cannabinoids, have varying structures and mechanisms of action that lead to different therapeutic effects and side effects. Cocaine, with its molecular formula C17H21NO4, is a tropane alkaloid and a tertiary amino compound. It has two chemical forms: the hydrochloride salt and the "freebase." The former is in powder form, while the latter involves removing the hydrochloride salt to create a form that can be smoked. Cocaine exerts its...
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相关实验视频

Updated: Mar 16, 2026

Ultrasonic-Assisted Extraction of Cannabidiolic Acid from Cannabis Biomass
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Ultrasonic-Assisted Extraction of Cannabidiolic Acid from Cannabis Biomass

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可解释的机器学习来识别大麻素1受体激动剂.

Sherwin S S Ng1, Yuk Lin Yong1, Justin S Y Tan1

  • 1Forensics Centre of Expertise, Home Team Science and Technology Agency, 138507, Singapore.

Forensic science international
|March 14, 2026
PubMed
概括

机器学习模型可以预测CB1受体中的合成大麻素受体激动剂 (SCRA) 活性. 这种方法有助于为生物测试优先考虑化合物,提高识别新型SCRA的效率.

科学领域:

  • 药用化学 医学化学
  • 计算化学计算化学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 合成大麻素受体激活剂 (SCRA) 与CB1受体相互作用,可能比天然激活剂产生更大的精神活性.
  • 通过传统的生物测试来识别新型SCRA通常是耗时且低效的.
  • 机器学习 (ML) 提供了一个有希望的补充方法,用于在药物发现中优先考虑化合物.

研究的目的:

  • 开发和评估用于预测分子中CB1受体激素活性的机器学习算法.
  • 在中度不平衡的数据集上评估ML模型的性能.
  • 利用可解释性技术来解释模型预测及其与化学结构的关系.

主要方法:

  • 探索各种机器学习算法来对CB1激动剂活性进行二元分类.
  • 模型培训和验证使用22%的少数阶级代表数据集.
  • 应用沙普利值用于模型可解释性和关键结构特征的识别.

主要成果:

  • 实现了0.937 (准确度),0.814 (精度) 和0.933 (回忆) 的中位数预测得分.
  • 通过广泛的评估,确定了开发的预测模型的局限性.
  • 沙普利值与传统的结构-活性关系 (SAR) 研究显示一致.
关键词:
类大麻素是一种大麻素.药物 药物 药物 是一种药物.可以解释的可解释性.机器学习 机器学习

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Oromucosal as an Alternative Method for Administration of Cannabis Products in Rodents
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Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B
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Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

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Oromucosal as an Alternative Method for Administration of Cannabis Products in Rodents
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Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B
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Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

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结论:

  • 机器学习模型可以有效预测CB1受体激动剂活性,有助于发现新型SCRAs.
  • 可解释的AI方法,像Shapley值一样,为CB1激动的化学驱动器提供了洞察力.
  • 这种基于ML的方法提高了识别潜在的治疗性或非法SCRA的效率.