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

Ogive Graph01:07

Ogive Graph

6.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.6K
Graphing Antiderivatives01:30

Graphing Antiderivatives

33
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
33
Bar Graph01:07

Bar Graph

21.4K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.4K
Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
Multiple Bar Graph01:07

Multiple Bar Graph

8.9K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
8.9K
First Derivatives and the Shape of a Graph01:22

First Derivatives and the Shape of a Graph

40
In calculus, the concept of the first derivative plays a crucial role in understanding the behavior of a function over its domain. The first derivative, denoted as f’(x), provides insight into how a function changes at any given point, much like a cyclist adjusting speed along a winding trail. By analyzing the first derivative, mathematicians can determine where a function is increasing, decreasing, or reaching critical points.The first derivative provides a precise method for classifying...
40

You might also read

Related Articles

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

Sort by
Same author

Cyphenothrin disrupts the integrity of porcine trophectoderm and uterine luminal epithelial cell lines by inducing mitochondrial dysfunction and oxidative stress.

Chemosphere·2026
Same author

Synthesis of 3-desoxycollinoketone B and its ability to reduce Alzheimer-associated misfolded proteins.

Nature communications·2026
Same author

Riemannian denoising model for molecular structure optimization with chemical accuracy.

Nature computational science·2026
Same author

Engineering Excited States of Pt-Based Deep-Blue Phosphors to Enhance OLED Stability.

ACS omega·2025
Same author

MetalloGen: Automated 3D Conformer Generation for Diverse Coordination Complexes.

Journal of chemical information and modeling·2025
Same author

SHARP: Generating Synthesizable Molecules via Fragment-Based Hierarchical Action-Space Reinforcement Learning for Pareto Optimization.

Journal of chemical information and modeling·2025

Related Experiment Video

Updated: Jan 20, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

438

Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph

Jaechang Lim1, Seongok Ryu1, Kyubyong Park2

  • 1Department of Chemistry , KAIST , Daejeon 34141 , South Korea.

Journal of Chemical Information and Modeling
|August 25, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new deep learning method using graph neural networks to predict drug-target interactions. This approach accurately identifies binding affinities and molecular poses, outperforming existing computational techniques.

More Related Videos

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.6K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

2.1K

Related Experiment Videos

Last Updated: Jan 20, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

438
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.6K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

2.1K

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Artificial intelligence in pharmacology

Background:

  • Accurate prediction of drug-target interactions (DTI) is crucial for efficient drug discovery.
  • Existing computational methods, including docking and some deep learning models, face limitations in accurately capturing complex intermolecular interactions.
  • There is a need for advanced computational models that can learn intricate features from 3D structural data for improved DTI prediction.

Purpose of the Study:

  • To propose a novel deep learning approach for predicting drug-target interactions.
  • To develop a distance-aware graph attention algorithm to distinguish different types of intermolecular interactions.
  • To leverage 3D structural information of protein-ligand binding poses for enhanced feature extraction.

Main Methods:

  • Utilized a graph neural network (GNN) architecture for DTI prediction.
  • Introduced a distance-aware graph attention mechanism to model intermolecular interactions.
  • Extracted graph features directly from the 3D structural data of protein-ligand complexes.

Main Results:

  • The proposed model achieved superior performance in virtual screening, with an AUROC of 0.968 on the DUD-E test set.
  • The model demonstrated high accuracy in pose prediction, achieving an AUROC of 0.935 on the PDBbind test set.
  • The method effectively learned key interaction features, outperforming traditional docking and other deep learning techniques, and could reproduce natural distributions of active and inactive molecules.

Conclusions:

  • The novel deep learning approach effectively predicts drug-target interactions by learning from 3D structural information.
  • The distance-aware graph attention mechanism enhances the model's ability to differentiate intermolecular interactions.
  • This method offers a significant advancement over existing computational tools for virtual screening and pose prediction in drug discovery.