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

Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

775
Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
775
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

248
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...
248
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.6K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.6K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

5.5K
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....
5.5K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.1K
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...
1.1K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

7.2K
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...
7.2K

You might also read

Related Articles

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

Sort by
Same author

Evaluation of the therapeutic effect of new hypoglycemic drugs on patients with heart failure with reduced ejection fraction and type 2 diabetes: a systematic review and network meta-analysis.

Frontiers in cardiovascular medicine·2026
Same author

A Multisource Transformer-Guided Graph Representation Learning Framework for circRNA-Disease Association Prediction.

ACS omega·2025
Same author

Survey and analysis of the prevalence of tobacco use among patients with severe mental illness at a tertiary specialized psychiatric medical center in China.

Frontiers in psychiatry·2025
Same author

Incorporating graph representation and mutual attention mechanism for MiRNA-MRNA interaction prediction.

Frontiers in genetics·2025
Same author

iHofman: a predictive model integrating high-order and low-order features with weighted attention mechanisms for circRNA-miRNA interactions.

BMC biology·2025
Same author

Predicting circRNA-Disease Associations through Multisource Domain-Aware Embeddings and Feature Projection Networks.

Journal of chemical information and modeling·2025

Related Experiment Video

Updated: Aug 23, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.8K

PPAEDTI: Personalized Propagation Auto-Encoder Model for Predicting Drug-Target Interactions.

Yue-Chao Li, Zhu-Hong You, Chang-Qing Yu

    IEEE Journal of Biomedical and Health Informatics
    |October 27, 2022
    PubMed
    Summary

    The PPAEDTI model accurately predicts drug-target interactions using graph personalized propagation. This computational tool aids drug discovery and repurposing, achieving over 90% AUC on benchmark datasets.

    More Related Videos

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

    2.0K
    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
    08:31

    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

    Published on: December 1, 2020

    5.1K

    Related Experiment Videos

    Last Updated: Aug 23, 2025

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
    10:21

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

    Published on: February 23, 2024

    2.8K
    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

    2.0K
    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
    08:31

    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

    Published on: December 1, 2020

    5.1K

    Area of Science:

    • Computational biology
    • Pharmacology
    • Bioinformatics

    Background:

    • Identifying drug targets is crucial for drug repurposing and development.
    • In vitro trials are costly and time-consuming.
    • Current computational methods for drug-target interaction prediction lack accuracy and efficiency.

    Purpose of the Study:

    • To develop an accurate and efficient computational model for predicting drug-target interactions.
    • To improve drug discovery and repurposing processes.

    Main Methods:

    • Proposed the PPAEDTI model, utilizing graph personalized propagation.
    • Trained and evaluated the model on six benchmark datasets.
    • Employed 5-fold cross-validation for performance assessment.

    Main Results:

    • The PPAEDTI model achieved average AUCs greater than 90% on five datasets.
    • Experimental validation confirmed high accuracy for predicted drug-target interactions.
    • A computational platform and associated resources were released.

    Conclusions:

    • The PPAEDTI model offers a valuable tool for predicting novel drug-target interactions.
    • The model's accuracy and efficiency support its application in pharmacology research.
    • The released platform facilitates further research in drug discovery and repurposing.