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相关概念视频

Adrenergic Receptors: ɑ Subtype01:31

Adrenergic Receptors: ɑ Subtype

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Adrenoceptors are classified into α and ꞵ classes based on their potencies to catecholamine agonists. α-adrenoceptors show the following order of catecholamine potency:
Adrenaline ≥ Noradrenaline >> Isoprenaline
α-adrenoceptors are further divided into α1 and α2-adrenoceptors.
α1-Adrenoceptors: These receptors are located postsynaptically on the effector organs and cause constriction of smooth muscle mediated by activation of phospholipase...
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Adrenergic Agonists: Direct-Acting Agents01:30

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Drugs that mimic the action of endogenous catecholamines like noradrenaline and adrenaline are called adrenergic agonists or sympathomimetics. Based on their mechanism of action, sympathomimetics can be classified as direct-, indirect-, or mixed-acting sympathomimetics. Direct-acting adrenergic agonists activate adrenoceptors without affecting presynaptic neurons, making them independent of neuronal catecholamine-depleting agents like reserpine and guanethidine.
These agents can be classified...
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Adrenergic Agonists: Therapeutic Uses01:30

Adrenergic Agonists: Therapeutic Uses

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Adrenergic agonists have diverse therapeutic uses across various medical conditions and emergencies.
Emergency and Intensive Care Unit (ICU) applications: Pressor agents increase blood pressure, heart rate, and contractility in shock and organ failure situations. Dopamine can induce vasodilation and stimulate adrenoceptors. Endogenous catecholamines are effective in treating cardiogenic shock. α2-agonists like clonidine can reverse anesthesia-induced hypertension.
Allergies and...
825
Adrenergic Receptors (Adrenoceptors): Classification01:27

Adrenergic Receptors (Adrenoceptors): Classification

2.6K
Adrenergic receptors, or adrenoceptors, respond to the autonomic neurotransmitter noradrenaline and other endogenous catecholamine agonists. They are classified into two main families, α and β, based on their pharmacological response and are further subdivided depending on their location, elicited response, and affinity to specific agonists or antagonists.
α-Adrenoceptors
α-Adrenoceptors are classified into two main subtypes: α1 and α2. The α1 adrenoceptors,...
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Adrenergic Antagonists: ɑ and β-Receptor Blockers01:31

Adrenergic Antagonists: ɑ and β-Receptor Blockers

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Third-generation β-blockers, such as labetalol and carvedilol, represent a significant advancement in managing cardiovascular conditions. Unlike conventional β-blockers, which can induce peripheral vasoconstriction, third-generation drugs block α1 adrenoceptors. This promotes vasodilation through several mechanisms, such as increased nitric oxide production, inhibition of calcium ion entry, opening of potassium ion channels, and antioxidant action. Labetalol, for instance, is...
497
Adrenergic Antagonists: Chemistry and Classification of ɑ-Receptor Blockers01:17

Adrenergic Antagonists: Chemistry and Classification of ɑ-Receptor Blockers

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Adrenergic antagonists, or sympatholytics, inhibit adrenoceptor activation driven by catecholamines or agonists. Based on their adrenoceptor specificity, adrenergic blockers can be categorized into two primary groups: α-adrenergic blockers (α-blockers) and β-adrenergic blockers (β-blockers). α-blockers interact with α1 and α2 subtypes of α-adrenoceptors.
Nonselective α-blockers: Nonselective α-blockers contain haloalkylamine or imidazoline...
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DeepADRA2A:使用深度学习预测上腺体α2a抑制剂.

Nitin Wankhade1, Ummireddy Dayasagar1, Anju Sharma1

  • 1Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sahibzada Ajit Singh Nagar, Punjab, India.

Journal of biomolecular structure & dynamics
|October 14, 2023
PubMed
概括

人工智能模型准确地预测了上腺素α2a (ADRA2A) 受体抑制剂,加速了药物发现. 深度学习模型的准确度超过98%,为传统方法提供更快的替代方案.

关键词:
机器学习 (ML) 是指机器学习.上腺作用α2a抑制剂注意缺陷多动障碍 (ADHD) 是一种注意缺陷多动障碍.深度学习 (DL) 是指深度学习.

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科学领域:

  • 药理学 药理学是指药理学的学科.
  • 计算化学计算化学
  • 人工智能在药物发现中的作用

背景情况:

  • 上腺素α2a (ADRA2A) 受体调节重要生理功能,包括血压和心率.
  • ADRA2A的失调与高血压和其他心血管疾病有关.
  • 识别ADRA2A抑制剂对于治疗相关疾病至关重要.

研究的目的:

  • 开发精确的人工智能 (AI) 模型,用于预测上腺素α2a (ADRA2A) 受体抑制剂.
  • 为传统药物发现方法提供更快,更具成本效益的替代方案.
  • 为了加快针对ADRA2A的潜在治疗剂的识别.

主要方法:

  • 采用了四种机器学习 (ML) 和深度学习 (DL) 算法.
  • 使用多种分子描述器 (1D,2D和分子指纹) 进行模型训练.
  • 对训练和测试数据集的评估模型性能.

主要成果:

  • 基于深度学习 (DL) 的模型表现出卓越的预测性能.
  • 在训练数据集上达到98.25%的高准确率,在测试数据集上达到97.23%.
  • 证明了DL在识别ADRA2A抑制剂方面的有效性.

结论:

  • 深度学习模型为预测上腺素α2a (ADRA2A) 抑制剂提供了强大而有效的工具.
  • 人工智能驱动的方法可以显著简化药物发现和开发过程.
  • 开发的模型是公开的,以促进进一步的研究.