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-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 Discovery: Overview01:26

Drug Discovery: Overview

8.3K
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...
8.3K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

959
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...
959
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
Protein Networks02:26

Protein Networks

4.1K
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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
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

The Association Between Childhood Maltreatment and Adult Intestinal Disorders: A Three-Level Meta-Analysis.

Trauma, violence & abuse·2026
Same author

Multimodal Drug-Target Affinity Prediction Via FastKAN-Based Hierarchical Fusion of Sequence, Structure, and Tabular Features.

IEEE journal of biomedical and health informatics·2026
Same author

Digital heart initiative: an ecosystem for digital discovery and precision medicine in cardiology.

National science review·2026
Same author

Mechanistic study on hyaluronic acid polysiloxane gel promoting wound repair guided by wet healing theory.

Biomedical materials (Bristol, England)·2026
Same author

IHGCN-PLA: An interpretable heterogeneous graph convolutional network for protein-ligand binding affinity prediction with multimodal interaction fusion.

Journal of biomedical informatics·2026
Same author

Comparative profiles of pediatric Mendeliome: A Single-Centre 572-Whole-Exome Sequencing Study in Xinjiang.

Human heredity·2026

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

A novel method for drug-target interaction prediction based on graph transformers model.

Hongmei Wang1, Fang Guo1, Mengyan Du1

  • 1College of Computer Science and Engineering, Changchun University of Technology, Changchun, China.

BMC Bioinformatics
|November 4, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for predicting drug-target interactions (DTIs) by modeling relationships independently. It utilizes line graphs and graph transformer networks for enhanced drug discovery and repositioning.

Keywords:
Drug-target interactionGraph attention networkLine graph

More Related Videos

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.9K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.7K

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
High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.9K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.7K

Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Drug-target interactions (DTIs) are crucial for drug research and repositioning.
  • Existing drug-target interaction network models often focus on individual drug or target nodes, neglecting their interrelationships.
  • There is a need for methods that capture the complex relationships within drug-target networks.

Purpose of the Study:

  • To propose a novel prediction method for modeling drug-target relationships independently.
  • To enhance the accuracy and efficiency of drug-target interaction prediction.
  • To facilitate accelerated drug research and drug repositioning.

Main Methods:

  • Constructing drug-target interaction features using multi-level drug and target relationships.
  • Employing line graphs to represent drug-target interactions, transforming links into nodes.
  • Introducing a graph transformer network for predicting drug-target interactions.

Main Results:

  • The proposed method effectively models drug-target relationships by considering their interactions.
  • The use of line graphs and graph transformer networks shows promise in predicting DTIs.
  • This approach offers a new perspective on analyzing drug-target interaction networks.

Conclusions:

  • The study presents a novel approach using line graphs and graph transformer networks for DTI prediction.
  • By transforming interactions into nodes, the method allows for a different way to analyze these relationships.
  • This facilitates more effective prediction of drug-target interactions, aiding drug discovery efforts.