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

Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

40
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
40
Drug toxicity: Drug–Drug Interaction01:30

Drug toxicity: Drug–Drug Interaction

80
Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
80
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

298
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...
298
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.3K
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...
2.3K
Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

532
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...
532
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.9K
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...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in spatial transcriptomics.

Briefings in bioinformatics·2026
Same author

Tandem repeat polymorphisms are associated with brain structure: results of two large population-based studies.

Genome medicine·2026
Same author

HTZ-1/H2A.Z expression sustains transcriptional programs that regulate Caenorhabditis elegans lifespan.

Mechanisms of ageing and development·2026
Same author

Advancing clinical precision medicine via peripheral blood immune single-cell omics.

Clinical and translational medicine·2026
Same author

In Vitro Cell Viability and Migration Inhibitory Effects of Isorhamnetin in Non-Small Cell Lung Cancer Cells.

Biomedicines·2026
Same author

3,3'-Di-O-methylellagic Acid Isolated from <i>Euphorbia humifusa</i> Willd Suppresses Prostate Cancer Cell Viability via Regulating VDAC1 Protein Expression.

Pharmaceuticals (Basel, Switzerland)·2026

Related Experiment Video

Updated: Feb 26, 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

1.7K

Knowledge-graph-enhanced multi-scale modeling for drug-drug interaction prediction.

Jing Chen1, Qiang Deng2,3, Peimeng Zhen2,3

  • 1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.

Molecular Therapy. Nucleic Acids
|February 25, 2026
PubMed
Summary

Predicting drug-drug interactions (DDIs) is vital for patient safety. Our new ALG-DDI model integrates multi-scale drug information, significantly improving DDI prediction accuracy over existing methods.

Keywords:
MT: Bioinformaticsdeep learningdrug-drug interactionknowledge graphmulti-scale feature fusiontransformer encoder

More Related Videos

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

4.7K
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

2.2K

Related Experiment Videos

Last Updated: Feb 26, 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

1.7K
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

4.7K
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

2.2K

Area of Science:

  • Pharmacology and Bioinformatics
  • Artificial Intelligence in Medicine

Background:

  • Drug-drug interaction (DDI) prediction is essential for preventing adverse drug events.
  • Current machine learning and deep learning models struggle with generalizing and capturing comprehensive drug relationships.

Purpose of the Study:

  • To develop an advanced model, ALG-DDI, for accurate drug-drug interaction prediction.
  • To integrate diverse drug information sources for a more holistic approach to DDI analysis.

Main Methods:

  • Proposed ALG-DDI, a multi-scale feature fusion model.
  • Integrated drug attribute, local correlations (protein, disease), and global semantic information (PrimeKG).
  • Employed attribute masking, RGCN, GraphSAGE, ComplEx, and a transformer encoder for feature representation and fusion.

Main Results:

  • ALG-DDI demonstrated superior performance compared to state-of-the-art methods across three datasets.
  • Extensive evaluations confirmed the model's effectiveness, including cross-validation and case studies.
  • The model successfully extended to drug-drug interaction event prediction.

Conclusions:

  • ALG-DDI effectively captures multi-scale drug information for improved DDI prediction.
  • The proposed model offers a robust solution for identifying potential adverse drug events.
  • This approach advances the field of computational pharmacology and personalized medicine.