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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...
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,...
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 Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
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...

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Related Experiment Video

Updated: Jun 14, 2026

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
13:18

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published on: March 3, 2023

Predicting drug-target interaction networks based on functional groups and biological features.

Zhisong He1, Jian Zhang, Xiao-He Shi

  • 1CAS-MPG Partner Institute of Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

Plos One
|March 20, 2010
PubMed
Summary

Predicting drug-target interactions computationally is crucial for efficient drug development. This study developed a novel in silico system using machine learning to accurately predict compound-protein interactions, achieving high success rates.

Related Experiment Videos

Last Updated: Jun 14, 2026

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
13:18

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published on: March 3, 2023

Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Drug-target interaction network analysis is vital for drug development.
  • Experimental determination of compound-protein interactions is time-consuming and expensive.
  • In silico prediction methods offer a timely and cost-effective alternative.

Purpose of the Study:

  • To develop an efficient in silico system for predicting drug-target interactions.
  • To enhance the speed and reduce the cost of drug discovery pipelines.
  • To provide valuable insights into potential compound-protein relationships.

Main Methods:

  • Compounds were encoded using functional groups; proteins were encoded using biochemical and physicochemical properties.
  • Feature selection was performed using the Maximum Relevance Minimum Redundancy (mRMR) method.
  • Four independent Nearest Neighbor predictors were developed for enzymes, ion channels, G-protein-coupled receptors, and nuclear receptors.

Main Results:

  • The developed predictors achieved high success rates in jackknife cross-validation tests.
  • Success rates for the four predictor groups were 85.48% (enzymes), 80.78% (ion channels), 78.49% (G-protein-coupled receptors), and 85.66% (nuclear receptors).
  • The system demonstrated effective prediction of drug-target interactions across diverse protein families.

Conclusions:

  • The established network prediction system shows significant promise for drug development.
  • The in silico approach provides a valuable tool for identifying potential drug-target interactions.
  • This method offers an encouraging complement to experimental drug discovery processes.