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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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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Principles of Drug Action01:24

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Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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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.
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Drug Administration and Therapy Phases: Overview01:26

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Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
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Updated: Jan 16, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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可解释的人工智能:药物发现的视角

Yazdan Ahmad Qadri1, Sibhghatulla Shaikh2,3, Khurshid Ahmad4

  • 1School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.

Pharmaceutics
|September 27, 2025
PubMed
概括
此摘要是机器生成的。

可解释的人工智能 (XAI) 提高了人工智能驱动的药物发现的透明度,解决了"黑子"问题. XAI方法加快治疗目标的识别和简化药物开发管道.

关键词:
人工智能的人工智能是人工智能.发现药物的发现.可解释的人工智能分子建模分子建模个性化医疗是个性化的医疗.治疗创新是一种治疗创新.

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科学领域:

  • 计算化学是一种计算化学.
  • 药理学 药理学是指药理学的学科.
  • 人工智能的人工智能是人工智能.

背景情况:

  • 人工智能 (AI) 和深度学习 (DL) 加快了药物发现,但存在"黑子"问题,阻碍了制药研究人员的采用.
  • 可解释的人工智能 (XAI) 通过增加对AI模型的透明度和信任来提供解决方案.

研究的目的:

  • 系统地审查XAI的原则,方法和药物发现工具.
  • 探索XAI在加速药物发现管道的各个阶段的应用.
  • 检查XAI如何解决AI模型不透明性及其挑战.

主要方法:

  • 系统地调查XAI的原则和方法.
  • 审查XAI工具,模型和药物发现框架.
  • 深入讨论XAI在医疗保健和药物开发中的应用.

主要成果:

  • 在AI驱动的药物发现中,XAI提高了透明度,信任和可靠性.
  • XAI的应用包括分子建模,目标识别,ADME预测和临床试验设计.
  • XAI有效地弥合了计算预测和制药应用之间的差距.

结论:

  • 在药物发现中,XAI对于克服AI的"黑盒"性质至关重要.
  • 需要进一步的研究来应对部署XAI方法的挑战.
  • 保持最新的XAI技术对于提高药物发现效率和临床影响至关重要.