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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

684
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...
684
Pharmacovigilance01:19

Pharmacovigilance

795
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...
795
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

960
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Drug-Receptor Bonds01:25

Drug-Receptor Bonds

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Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
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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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Drug Discovery: Overview01:26

Drug Discovery: Overview

7.7K
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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Updated: Jun 15, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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基于RDF知识图的机器学习用于药物安全:对Reactome数据的案例研究

Kalliopi Kastampolidou1, George I Gavriilidis1, Pantelis Natsiavas1

  • 1Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece.

Studies in health technology and informatics
|August 23, 2024
PubMed
概括

在知识图表上将象征性人工智能 (AI) 与机器学习 (ML) 集成,可以增强药物安全洞察力. 利用RDF/OWL等语义网络技术与ML显示出有希望的结果.

关键词:
知识图是知识图.机器学习就是机器学习.存在学 (Ontologies) 是一种存在学.反应原子是如何反应的语义学 是一个语义学.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能
  • 语义网络技术 语义网络技术

背景情况:

  • 人工智能 (AI) 和机器学习 (ML) 越来越多地应用于科学领域.
  • 将象征性AI与ML集成,特别是在知识图表上,仍然是一个未经探索的领域.
  • 利用RDF/OWL等语义网络技术与ML一起,可以获得更深入的见解.

研究的目的:

  • 在知识图上研究象征性AI (RDF/OWL语义) 与机器学习 (ML) 的集成.
  • 探索这种综合方法的应用,以加强药物安全性分析.
  • 为了证明在ML工作流中利用语义网络语义的实用性.

主要方法:

  • 开发了一种结合机器学习 (ML) 与RDF/OWL在知识图上的语义推理的方法.
  • 作为一个特定的用例,利用了来自Reactome数据库的信号通路数据.
  • 应用ML技术来分析语义信息以预测药物安全性.

主要成果:

  • 综合方法在探索药物安全方面产生了有希望的结果.
  • 在ML任务中展示了利用RDF/OWL语义的价值.
  • 涉及Reactome信号通路的用例显示了发现新见解的潜力.

结论:

  • 将象征性AI和ML集成到知识图中是科学发现的一个有希望的方向.
  • 在ML工作流中利用RDF/OWL语义可以提供宝贵的见解,特别是在药物安全等领域.
  • 建议进行进一步的研究和与领域专家的合作,以充分发挥这种方法的潜力.