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

1.6K
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...
1.6K
Drug Discovery: Overview01:26

Drug Discovery: Overview

10.9K
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...
10.9K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.7K
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...
1.7K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

223
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
223
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

4.1K
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...
4.1K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.8K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.8K

You might also read

Related Articles

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

Sort by
Same author

tRNA is a molecular thermometer of species' optimal growth temperature.

International journal of biological macromolecules·2025
Same author

Exploring species taxonomic kingdom using information entropy and nucleotide compositional features of coding sequences based on machine learning methods.

Methods (San Diego, Calif.)·2025
Same author

iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations.

IET systems biology·2024
Same author

Proteomics·2024
Same author

SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations.

Current research in structural biology·2024
Same author

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations.

Computers in biology and medicine·2023

Related Experiment Video

Updated: Jan 7, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.1K

RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.

Biffon Manyura Momanyi1,2, Sebu Aboma Temesgen1, Bakanina Kissanga Grace-Mercure1

  • 1School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.

Current Drug Targets
|January 5, 2026
PubMed
Summary

This study introduces RWRGDR, a novel framework using Graph Neural Networks and Random Walk with Restart for identifying drug-disease interactions. It offers a reliable drug repositioning strategy, especially for conditions lacking treatments.

Keywords:
DrugGraphSAGEdiseasedrug repositioningrandom walk with restart.

More Related Videos

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

866
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

2.1K

Related Experiment Videos

Last Updated: Jan 7, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.1K
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

866
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

2.1K

Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug discovery

Background:

  • Traditional drug development is costly and slow.
  • Computational methods for drug-disease correlations are gaining traction.
  • Existing methods often underutilize network and drug-disease association data.

Purpose of the Study:

  • To propose the RWRGDR framework for unsupervised feature learning to identify potential drug-disease interactions.
  • To leverage Graph Neural Networks (GNN) and Random Walk with Restart (RWR) for enhanced prediction.
  • To improve drug repositioning strategies.

Main Methods:

  • Utilized GraphSAGE for low-dimensional representation encoding.
  • Employed Graph Attention Networks (GAT) for neighbor weighting.
  • Integrated Random Walk with Restart (RWR) for global network perspective.
  • Fused local features and long-range dependencies for superior predictions.

Main Results:

  • Achieved an Area Under the Curve (AUC) of 0.84 and Area Under the Precision-Recall Curve (AUPRC) of 0.91.
  • Demonstrated highly competitive performance, surpassing previous techniques.
  • Case studies validated the practical applicability and reliability of the RWRGDR framework.

Conclusions:

  • Comprehensive network exploration enhances understanding of complex interactions for optimized predictions.
  • The RWRGDR model excels in prioritizing highly ranked minority positives, indicated by a superior AUPRC.
  • RWRGDR presents a viable drug repositioning strategy, particularly for emerging diseases with unmet needs.