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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

720
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...
720
Protein Networks02:26

Protein Networks

4.0K
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,...
4.0K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.9K
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...
7.9K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

6.3K
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...
6.3K
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

2.8K
Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Comparison of systolic and diastolic CT-FFR for myocardial ischemia diagnosis.

BMC medical imaging·2026
Same author

Anatomy-Guided Spatiotemporal Affinity Learning for Unsupervised Domain Adaptation in Echocardiography Segmentation.

IEEE journal of biomedical and health informatics·2026
Same author

Enhanced Thermal Polycondensation of Heavy Coal Tar to Mesophase Pitch via Polyethylene Modification.

Polymers·2026
Same author

Conveyor belt foreign object detection method based on improved YOLOv11 and ESRGAN.

Scientific reports·2026
Same author

Ferroptosis in musculoskeletal disorders: Emerging mechanisms and therapeutic opportunities (Review).

International journal of molecular medicine·2026
Same author

Integrating MRI habitat heterogeneity and peritumoral radiomics into a nomogram for optimized risk stratification in locally advanced rectal cancer: a multicenter study.

Frontiers in oncology·2026

Related Experiment Video

Updated: Jul 3, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.2K

GSL-DTI: Graph structure learning network for Drug-Target interaction prediction.

Zixuan E1, Guanyu Qiao1, Guohua Wang1

  • 1College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China.

Methods (San Diego, Calif.)
|February 15, 2024
PubMed
Summary

This study introduces GSL-DTI, an automatic graph structure learning model for drug-target interaction prediction. It enhances accuracy by learning the drug-protein pair network structure automatically, outperforming existing methods.

Keywords:
Drug-Protein Pair (DPP)Drug-Target InteractionGraph Structure LearningHeterogeneous Information Networks

More Related Videos

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
13:18

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published on: March 3, 2023

1.2K
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

1.6K

Related Experiment Videos

Last Updated: Jul 3, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.2K
Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
13:18

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published on: March 3, 2023

1.2K
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

1.6K

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning

Background:

  • Drug-target interaction (DTI) prediction is crucial for drug discovery, accelerating the identification of potential drug candidates.
  • Traditional drug discovery is time-consuming, expensive, and high-risk, making computational prediction methods essential.
  • Current graph-based DTI prediction methods often rely on manual rules for network construction, failing to capture complex relationships.

Purpose of the Study:

  • To propose GSL-DTI, an automatic graph structure learning model for enhanced drug-target interaction prediction.
  • To overcome the limitations of manually defined rules in constructing drug-protein pair networks.
  • To improve the accuracy and efficiency of DTI prediction using advanced graph learning techniques.

Main Methods:

  • Integrating large-scale heterogeneous networks using a graph convolution network based on meta-paths to learn drug and protein representations.
  • Introducing an automatic graph structure learning approach using a filter gate on affinity scores and classification loss to guide network construction.
  • Transforming DTI prediction into a node classification problem on the learned drug-protein pair network.

Main Results:

  • GSL-DTI demonstrates superior performance in DTI prediction tasks across three public datasets.
  • The automatic graph structure learning approach effectively captures underlying relationships between drugs and target proteins.
  • The model provides a novel perspective for advancing graph structure learning in DTI prediction.

Conclusions:

  • GSL-DTI offers a more effective and accurate method for predicting drug-target interactions compared to existing approaches.
  • Automatic graph structure learning is a promising direction for improving computational drug discovery tools.
  • The proposed model has the potential to significantly expedite the drug discovery and development pipeline.