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相关概念视频

Drug Biotransformation: Overview01:16

Drug Biotransformation: Overview

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Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
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Drug Nomenclature01:17

Drug Nomenclature

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During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
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Drug Classes and Categories01:25

Drug Classes and Categories

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Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

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Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
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Pharmacovigilance01:19

Pharmacovigilance

903
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
903
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

3.1K
An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
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相关实验视频

Updated: Jul 24, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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多标签分类与双尾节点增强用于药物重新定位.

Xinyu Zhu, Weiming Lu

    IEEE/ACM transactions on computational biology and bioinformatics
    |July 7, 2023
    PubMed
    概括

    这项研究引入了一种新的模型,即药物重新定位的尾部节点增强 (TNA-DR),通过专注于代表性不足的药物疾病联系来改善药物重新定位. 该模型增强了尾部节点的数据增强,促进了新药-疾病关联的发现.

    科学领域:

    • 计算生物学是一种计算生物学.
    • 药理学 药理学是指药理学的学科.
    • 机器学习 机器学习

    背景情况:

    • 药物发现是漫长而昂贵的,推动了对药物重新定位的兴趣.
    • 目前用于药物重新定位的机器学习方法面临的挑战是缺乏训练数据和忽视尾部节点.

    研究的目的:

    • 为药物重新定位提出一种新的多标签分类模型.
    • 通过结合双尾节点增强来解决现有方法的局限性.

    主要方法:

    • 开发了用于药物重新定位的尾部节点增强 (TNA-DR) 模型.
    • 集成的k-最近邻居 (kNN) 和使用药物-药物和疾病-疾病相似性的对比增强模块.
    • 按度过节点,以集中增强尾部节点.

    主要成果:

    • 在四个现实数据集上实现了最先进的性能.
    • 在识别新疾病的候选药物的有效性已被证明.
    • 展示了发现新药与疾病相关性的能力.

    结论:

    • TNA-DR有效地补充了药物疾病协会的弱监督.

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  • 该模型通过优先考虑尾部节点来增强药物重新定位.
  • TNA-DR为高效的药物重新定位和发现提供了一个有前途的方法.