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相关概念视频

Drug Discovery: Overview01:26

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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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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...
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Drug Abuse and Addiction: Pharmacological Phenomena01:15

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Drug dependence, abuse, and addiction are complex phenomena that can precipitate various abnormal states. Physical dependence refers to a state of pharmacological adaptation to a drug. This adaptation often results in tolerance—a reduced response to the drug after repeated administrations. When the drug use is abruptly stopped, withdrawal symptoms occur due to the body's need to readjust from the pharmacologically induced imbalance. However, tolerance and withdrawal symptoms do not...
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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Nonlinear or dose-dependent pharmacokinetics is a phenomenon that occurs when the pharmacokinetic parameters of certain drugs deviate from linear pharmacokinetics at higher doses. These drugs do not follow the expected first-order kinetics, where the rate of drug elimination is directly proportional to the drug concentration. Instead, they exhibit a nonlinear relationship, which can be attributed to several factors.
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机器学习启用药物诱导毒性预测.

Changsen Bai1,2,3, Lianlian Wu1,2, Ruijiang Li2

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.

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概括
此摘要是机器生成的。

机器学习 (ML) 推进了药物毒性预测,克服了昂贵的动物试验. 本综述详细介绍了10种毒性类型的ML模型和数据库,帮助药物开发.

关键词:
我们的数据库数据库数据库数据库.深度学习是一种深度学习.药物毒性预测 药物毒性预测机器学习是机器学习.

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科学领域:

  • 计算毒理学计算毒理学
  • 药理学 药理学是指药理学的学科.
  • 药物发现 药物发现

背景情况:

  • 意想不到的药物毒性导致30%的发育失败.
  • 传统的动物试验是昂贵和缓慢的.
  • 人工智能 (AI) 和机器学习 (ML) 提供创新的毒理学解决方案.

研究的目的:

  • 对10个药物诱导毒性类别的ML模型进行审查.
  • 为了比较可预测和可解释的ML算法.
  • 突出用于毒性预测的关键数据库和工具.

主要方法:

  • 在毒理学中对ML应用的系统审查.
  • 对10种药物诱导毒性类别的分析.
  • 相关数据库和计算工具的总结.

主要成果:

  • 识别的最佳ML模型在毒性领域之间有所不同.
  • 描述了适用的预测和可解释的ML算法.
  • 按功能组织关键数据库,用于毒性预测.

结论:

  • 在推进药物诱导毒性预测方面,ML显示出显著的希望.
  • 对ML模型的比较分析对于最佳应用至关重要.
  • 资源的战略性使用可以弥合预测和机械洞察力.