Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

451
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...
451
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

7.5K
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....
7.5K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

10.4K
Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
10.4K
Pharmacokinetics: Drug–Food and Drug–Viral Interactions01:26

Pharmacokinetics: Drug–Food and Drug–Viral Interactions

255
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...
255
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

607
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.
Displacement interactions can have varying outcomes, ranging from toxicity to virtually...
607
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

5.1K
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...
5.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Detoxification of 3- and 15-acetyldeoxynivalenol and deoxynivalenol-3-glucoside by laccase Lac-W with acetosyringone.

Toxicon : official journal of the International Society on Toxinology·2026
Same author

Rapid In Vitro Pathological Diagnosis of Glioma Using Dual-Color Quantum Dot Probes for Precise Intraoperative Resection.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Spin-State Engineering in 2D Metal-Organic Frameworks for Ultrasensitive Room-Temperature Ammonia Sensing.

ACS sensors·2026
Same author

Correction: Factors associated with recurrence of mitral regurgitation 14 days after rheumatic mitral valve repair.

Frontiers in cardiovascular medicine·2026
Same author

Sweat-Based Wearable Electronic Devices for Health Monitoring: Materials, Devices, and Applications.

ACS sensors·2026
Same author

Reentrant superconductivity at an oxide heterointerface.

Science advances·2026

Related Experiment Video

Updated: Feb 6, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction.

Wen Zhang1, Weiran Lin1, Ding Zhang1

  • 1School of Computer Science, Wuhan University, Wuhan 430072, China.

Current Drug Metabolism
|August 22, 2018
PubMed
Summary

This review summarizes machine learning approaches for predicting drug-target interactions, a key step in drug discovery. It analyzes various datasets, features, and computational methods to guide future research.

Keywords:
Machine learningdrug discoverydrug repurposingdrug-target interactionmolecular fingerprintsimilarity measure.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

744
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

Related Experiment Videos

Last Updated: Feb 6, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

744
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Machine learning

Background:

  • Accurate drug-target interaction identification is vital for efficient drug discovery.
  • Significant advancements have been made in computational methods, databases, and software for predicting these interactions.

Purpose of the Study:

  • To review recent progress in machine learning-based drug-target interaction prediction.
  • To provide a guide for developing novel computational methods in this field.

Main Methods:

  • Summarizing and analyzing various datasets and data types relevant to drug-target interactions.
  • Detailing feature extraction methods for drugs and targets from diverse data sources.
  • Explaining similarity calculation techniques for drugs and targets, crucial for predictive models.
  • Comparing and contrasting different machine learning-based prediction methodologies.

Main Results:

  • An overview of current machine learning strategies for drug-target interaction prediction.
  • Analysis of how different data features and similarity metrics influence prediction models.
  • A comparative assessment of diverse machine learning approaches.

Conclusions:

  • The review offers a comprehensive guide for the advancement of computational methods in drug-target interaction prediction.
  • It highlights the importance of machine learning in accelerating drug discovery pipelines.