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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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 its...
Protein Networks02:26

Protein Networks

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,...
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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.
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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 Kd...
Drug toxicity: Drug–Drug Interaction01:30

Drug toxicity: Drug–Drug Interaction

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...

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Related Experiment Video

Updated: Jun 27, 2026

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

Multimodal Collaborative Modeling of Molecular Structures and Biomedical Text for Accurate Drug-Drug Interaction

Liumei Yang1, Yiyang Shi1, Fangfang Han1,2,3

  • 1School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.

Biomedicines
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces MultiMod-DDI, a novel framework for accurate drug-drug interaction (DDI) extraction from biomedical texts. It enhances DDI identification by aligning molecular structure, biological entities, and text data.

Keywords:
biomedical text miningdrug–drug interactions (DDIs)multimodal learning

Related Experiment Videos

Last Updated: Jun 27, 2026

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

Area of Science:

  • Biomedical Informatics
  • Computational Chemistry
  • Natural Language Processing

Background:

  • Drug-drug interactions (DDIs) are a major cause of adverse drug reactions and hospital deaths.
  • Extracting DDIs from complex biomedical texts is challenging due to limitations in existing methods.
  • Prior multimodal approaches lack deep alignment, leading to misclassification of similar drug pairs.

Purpose of the Study:

  • To develop an advanced framework for accurate drug-drug interaction (DDI) extraction from biomedical literature.
  • To address the challenge of identifying correct interaction types for drug pairs in complex, multi-drug sentences.
  • To improve the reliability of intelligent DDI mining from scientific texts.

Main Methods:

  • Proposed MultiMod-DDI framework constructing a "molecular structure-biological entities-DDI text" evidence chain.
  • Introduced PS-AEGNN with ProbSparse self-attention for molecular graph analysis.
  • Employed adaptive position interaction vectors and multi-stage adaptive fusion for multimodal alignment.

Main Results:

  • MultiMod-DDI achieved superior performance on SemEval-2013 Task 9, with 85.57% F1-macro and 85.20% F1-micro.
  • Outperformed existing state-of-the-art models in drug-drug interaction extraction.
  • Demonstrated effective resolution of mismatches between drug pairs and their interaction types.

Conclusions:

  • Multimodal deep semantic alignment in MultiMod-DDI significantly enhances DDI extraction accuracy.
  • The integration of heterogeneous multimodal features provides a reliable method for DDI mining.
  • This approach offers a robust solution for identifying drug-drug interactions in complex biomedical literature.