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Related Concept Videos

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,...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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...
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against specific...

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Related Experiment Video

Updated: Jun 28, 2026

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

Finding multiple target optimal intervention in disease-related molecular network.

Kun Yang1, Hongjun Bai, Qi Ouyang

  • 1Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.

Molecular Systems Biology
|November 6, 2008
PubMed
Summary

This study introduces a computational algorithm for identifying optimal multiple drug targets. This approach aims to improve disease control by finding effective and safer combination therapies.

Related Experiment Videos

Last Updated: Jun 28, 2026

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

Area of Science:

  • Computational biology
  • Systems pharmacology
  • Network medicine

Background:

  • Single-target drugs face limitations in disease control.
  • Identifying optimal multiple drug targets requires understanding complex disease networks.
  • Innovative computational methods are needed to analyze network structure and dynamics.

Purpose of the Study:

  • To develop a robust computational algorithm for finding multiple target optimal intervention (MTOI) solutions.
  • To identify potential drug targets and optimal intervention combinations for restoring disease networks to a normal state.
  • To apply the MTOI algorithm to an inflammation-related network.

Main Methods:

  • Development of a novel computational algorithm for MTOI.
  • Modeling of an inflammation-related network, incorporating known drug side effects.
  • Application of the MTOI algorithm to identify effective and safe intervention strategies.

Main Results:

  • The MTOI algorithm successfully identified potential drug targets and optimal intervention combinations.
  • The model accurately accounted for known side effects of anti-inflammatory drugs, including Vioxx.
  • Several promising MTOI solutions demonstrated both efficacy and improved safety profiles.

Conclusions:

  • The MTOI algorithm provides a powerful tool for systematic identification of multiple drug targets.
  • This network-based approach offers a pathway to designing more effective and safer combination therapies.
  • The findings suggest potential for improved disease management through optimized multi-target drug interventions.