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

Drug-Receptor Interactions01:29

Drug-Receptor Interactions

7.2K
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....
7.2K
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

942
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
942
Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

319
Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
319
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

4.6K
An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
4.6K
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

11.5K
The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
11.5K
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

6.7K
Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
6.7K

You might also read

Related Articles

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

Sort by
Same author

Explainable AI and optimized solar power generation forecasting model based on environmental conditions.

PloS one·2024
Same author

Green finance growth prediction model based on time-series conditional generative adversarial networks.

PloS one·2024
Same author

Many-objective African vulture optimization algorithm: A novel approach for many-objective problems.

PloS one·2023
Same author

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA-IncRNA Based on Artificial Gorilla Troops Algorithm.

Sensors (Basel, Switzerland)·2023
Same author

Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning.

International journal of imaging systems and technology·2021
Same author

Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt.

Environmental science and pollution research international·2021
Same journal

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026
Same journal

ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Journal of bioinformatics and computational biology·2026
Same journal

When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis.

Journal of bioinformatics and computational biology·2026
Same journal

Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction.

Journal of bioinformatics and computational biology·2026
Same journal

Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks.

Journal of bioinformatics and computational biology·2026
Same journal

Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.

Journal of bioinformatics and computational biology·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

Multi-Classification of Drug-Drug interaction based on a complete graph convolutional neural network and explainable

Samar Monem1,2, Ashraf Darwish2,3,4,5, Aboul Ella Hassanien2,6

  • 1Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef, Egypt.

Journal of Bioinformatics and Computational Biology
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a graph convolutional neural network (GCN) model to predict drug-drug interactions (DDIs), achieving 95.12% accuracy. The explainable AI approach enhances safety in multi-drug therapy by identifying potential drug hazards.

Keywords:
DrugBankDrug–drug interactions (DDIs)SHapley Additive exPlainations (SHAP)explainable artificial intelligence (XAI)graph convolutional neural (GCN)multi-classification

More Related Videos

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

795
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

19.4K

Related Experiment Videos

Last Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

795
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

19.4K

Area of Science:

  • Computational chemistry
  • Pharmacology
  • Artificial intelligence in medicine

Background:

  • Multi-drug therapy is increasingly common, particularly in older adults with multiple comorbidities.
  • Unanticipated drug-drug interactions (DDIs) pose significant risks, leading to adverse reactions and toxicity.
  • Computational models can predict DDIs, improving drug design and reducing research costs.

Purpose of the Study:

  • To develop and evaluate a novel computational model for predicting drug-drug interactions (DDIs).
  • To enhance the accuracy and efficiency of DDI prediction using a graph convolutional neural network (GCN).
  • To improve the interpretability of DDI prediction models through explainable artificial intelligence (XAI).

Main Methods:

  • A complete graph convolutional neural (GCN) network was constructed using publicly available DDI data from DrugBank.
  • The model processed 37,264 samples with three optimal features: chemical, target, and enzyme.
  • The multi-classification model involved drug preprocessing, three GCN layers, and a fully connected network.

Main Results:

  • The proposed GCN model achieved a high accuracy of 95.12% in DDI prediction, outperforming previous methods on the same dataset.
  • The model demonstrated improved computational time and classification evaluation metrics, even with imbalanced data.
  • Explainable artificial intelligence (XAI) techniques, specifically SHapley Additive exPlanations (SHAP), were applied for model interpretability.

Conclusions:

  • The developed GCN model effectively predicts DDIs with high accuracy and improved efficiency.
  • The integration of XAI enhances model transparency, aiding in the understanding of potential drug hazards.
  • This model offers a valuable tool for intelligent pharmaceutical management and mitigating risks associated with multi-drug therapy.