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

Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

4.8K
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...
4.8K
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

579
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...
579
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.2K
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...
1.2K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

6.0K
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....
6.0K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

9.3K
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...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Learning from heterogeneous structural MRI via collaborative domain adaptation for late-Life depression assessment.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Unbiased clustering of acute-on-chronic liver failure patients using machine learning in a real-world ICU cohort.

Nature communications·2026
Same author

Andrographolide inhibits the upregulation of SLC19A3 to block the adipogenic differentiation of adipose-derived stem cells.

iScience·2026
Same author

Predicting isocitrate dehydrogenase status in glioma using hierarchical attention-based deep 3D multiple instance learning.

Frontiers in oncology·2026
Same author

Hyperbolic Kernel Graph Neural Networks for Neurocognitive Decline Analysis From Multimodal Brain Imaging.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

DSA-DeepFM: a dual-stage attention-enhanced DeepFM model for predicting anticancer synergistic drug combinations.

Bioinformatics advances·2025

Related Experiment Video

Updated: Sep 15, 2025

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

18.8K

HIG-Syn: a hypergraph and interaction-aware multigranularity network for predicting synergistic drug combinations.

Yuexi Gu1, Jian Zu1, Yongheng Sun1

  • 1School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China.

Bioinformatics (Oxford, England)
|July 15, 2025
PubMed
Summary

We developed HIG-Syn, a novel deep learning model for predicting synergistic drug combinations. This model enhances accuracy and biological relevance, showing practical potential for drug discovery.

More Related Videos

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

12.0K
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

4.3K

Related Experiment Videos

Last Updated: Sep 15, 2025

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

18.8K
High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

12.0K
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

4.3K

Area of Science:

  • Computational Biology
  • Pharmacology
  • Artificial Intelligence

Background:

  • Drug combinations offer enhanced efficacy and reduced toxicity.
  • Large datasets from screening technologies enable deep learning for synergy prediction.
  • Current methods lack accuracy and biological interpretability.

Purpose of the Study:

  • To develop a more accurate and biologically interpretable deep learning model for predicting drug combination synergy.
  • To improve the practical application of computational methods in drug discovery.

Main Methods:

  • Proposed the HIG-Syn (hypergraph and interaction-aware multigranularity network) model.
  • Integrated coarse-granularity (hypergraph for global features) and fine-granularity (interaction-aware attention for biological processes) modules.
  • Modeled substructure-substructure and substructure-cell line interactions.

Main Results:

  • HIG-Syn outperformed state-of-the-art models on DrugComb and GDSC2 datasets.
  • Predicted 12 novel synergistic drug combinations.
  • Five of the 12 predicted combinations were supported by existing literature, demonstrating practical potential.

Conclusions:

  • HIG-Syn offers improved accuracy and biological insight for drug synergy prediction.
  • The model shows significant potential for identifying effective and safe drug combinations in preclinical research.
  • Further validation of predicted combinations is warranted.