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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Pharmacokinetics: Drug–Drug Interactions01:25

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Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
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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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Targets for Drug Action: Overview01:26

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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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Pharmacokinetics: Drug–Food and Drug–Viral Interactions01:26

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A drug interaction occurs when the concurrent use of another drug, food, or an external substance alters the pharmacological activity of a drug. This interaction can modify the action of the original drug, affecting its effectiveness and safety.Drug–food interactions are significant as they impact drug absorption, metabolism, and excretion. For example, grapefruit juice is a well-known disruptor of drug metabolism. It inhibits the cytochrome P450 3A4 enzyme, crucial for the metabolism of...
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Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

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Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
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Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
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Network-Based Methods for Prediction of Drug-Target Interactions.

Zengrui Wu1, Weihua Li1, Guixia Liu1

  • 1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.

Frontiers in Pharmacology
|October 26, 2018
PubMed
Summary

Network-based inference methods efficiently predict drug-target interactions (DTIs) without needing 3D structures. These computational approaches aid drug discovery, repurposing, and understanding drug mechanisms.

Keywords:
drug repurposingdrug-target interactionnetwork-based methodsystems pharmacologysystems toxicologytarget prediction

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Pharmacology

Background:

  • Drug-target interaction (DTI) prediction is crucial for drug discovery but experimentally expensive.
  • Computational methods offer efficient alternatives, with network-based approaches showing significant advantages.
  • Existing methods include molecular docking, pharmacophore, similarity, machine learning, and network-based techniques.

Purpose of the Study:

  • To review and highlight network-based methods for DTI prediction.
  • To focus on network-based inference (NBI) derived from recommendation algorithms.
  • To discuss applications and future perspectives of network-based DTI prediction.

Main Methods:

  • Focus on network-based methods for DTI prediction.
  • Introduction to Network-Based Inference (NBI) methodologies.
  • Evaluation metrics for network-based DTI prediction.

Main Results:

  • Network-based methods offer advantages by not requiring 3D target structures or negative samples.
  • NBI methods, adapted from recommendation algorithms, show promise for DTI prediction.
  • Applications span target prediction, elucidating therapeutic effects, and safety concerns.

Conclusions:

  • Network-based methods provide efficient and cost-effective tools for DTI prediction.
  • These methods are valuable for drug repurposing, new drug discovery, systems pharmacology, and systems toxicology.
  • Further research into limitations and perspectives will enhance their utility.