Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Quantitative Aspects of Drug-Receptor Interaction

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...

You might also read

Related Articles

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

Sort by

Same author

Expression of tissue factor pathway inhibitor-2 in gastric stromal tumor and its clinical significance.

Experimental and therapeutic medicine·2014

Same author

Facile access to cytocompatible multicompartment micelles with adjustable Janus-cores from A-block-B-graft-C terpolymers prepared by combination of ROP and ATRP.

Colloids and surfaces. B, Biointerfaces·2014

Same author

Functional layers for Zn(II) ion detection: from molecular design to optical fiber sensors.

The journal of physical chemistry. B·2013

Same author

Expression of the 78 kD glucose-regulated protein is induced by endoplasmic reticulum stress in the development of hepatopulmonary syndrome.

Gene·2013

Same author

Multi-nuclear silver(I) and copper(I) complexes: a novel bonding mode for bispyridylpyrrolides.

Dalton transactions (Cambridge, England : 2003)·2013

Same author

Transcriptome profilings of female Schistosoma japonicum reveal significant differential expression of genes after pairing.

Parasitology research·2013

Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026

Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 22, 2025

Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model

Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model

Published on: June 6, 2025

TreeQNet: a webserver for Treatment evaluation with Quantified Network.

Zhenlei Li^1,2, Ya Huang^3,4,5, Qingrun Li³

¹School of Computer Science and Technology, University of Science and Technology of China, Jinzhai Road 96, Hefei, 230027, People's Republic of China.

BMC Bioinformatics

|November 11, 2022

Summary

This summary is machine-generated.

Predicting patient drug sensitivity is key for personalized cancer therapy. TreeQNet, a novel webserver, uses proteomic and phosphoproteomic networks to forecast individual responses to cancer drugs.

Keywords:

Drug susceptibility prediction Kinase-phospho substrate networks Phosphoproteomic Proteomic Quantified Network

More Related Videos

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Published on: November 27, 2019

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

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

Published on: March 3, 2023

Related Experiment Videos

Last Updated: Aug 22, 2025

Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model

Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model

Published on: June 6, 2025

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Published on: November 27, 2019

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

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

Published on: March 3, 2023

Area of Science:

Oncology
Bioinformatics
Systems Biology

Background:

Personalized therapy improves cancer treatment effectiveness by tailoring drug selection to individual patient responses.
Accurate prediction of patient drug sensitivity is crucial for optimizing therapeutic agent application.
Current methods often overlook crucial molecular interactions, limiting their utility in complex scenarios like clinical cancer drug response prediction.

Purpose of the Study:

To develop an innovative computational tool for predicting patient-specific drug sensitivity in cancer.
To leverage network-based approaches integrating proteomic and phosphoproteomic data for enhanced predictive accuracy.

Main Methods:

Development of the TreeQNet webserver.
Utilizing proteomic and phosphoproteomic network data derived from tumor tissues.
Implementing a network-based approach to model and predict drug sensitivity.

Main Results:

TreeQNet successfully predicts patient sensitivity to various drugs.
The webserver integrates complex network information for improved prediction accuracy.
Demonstrates the utility of phosphoproteomic and proteomic networks in cancer drug response prediction.

Conclusions:

TreeQNet offers a valuable service for predicting cancer drug sensitivity.
The tool facilitates personalized medicine by providing patient-specific treatment insights.
Source code and service are available for further research and application.