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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.1K
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...
1.1K
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

979
Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
979
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.3K
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...
1.3K
Qualitative Analysis01:10

Qualitative Analysis

744
Qualitative analysis is the process of identifying elements, ions, or compounds in an unknown sample. It is the first and most fundamental type of analysis based on the hierarchy of analytical goals. This hierarchy is significant as it provides a structured approach to scientific research, with qualitative analysis serving as the initial step, providing essential information before moving on to quantitative or other forms of analysis.
There are two main approaches to qualitative analysis:...
744
Response Surface Methodology01:16

Response Surface Methodology

311
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
311
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

95
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
95

You might also read

Related Articles

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

Sort by
Same author

Active upper limb prostheses: a review on current state and upcoming breakthroughs.

Progress in biomedical engineering (Bristol, England)·2025
Same author

QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Molecular diversity·2021
Same author

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy.

Environment international·2019
Same author

QSAR: What Else?

Methods in molecular biology (Clifton, N.J.)·2018
Same author

QSAR Methods.

Methods in molecular biology (Clifton, N.J.)·2016
Same author

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds.

Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews·2016
Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Oct 3, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.1K

QSAR Methods.

Giuseppina Gini1

  • 1DEIB, Politecnico di Milano, Milan, Italy. giuseppina.gini@polimi.it.

Methods in Molecular Biology (Clifton, N.J.)
|February 21, 2022
PubMed
Summary
This summary is machine-generated.

Computational chemistry and in silico methods are replacing traditional experiments for predicting biological properties and toxicology. These modeling approaches, including quantitative structure-activity relationships (QSAR), deduce properties from chemical structures for drug discovery and chemical design.

Keywords:
ChemometricsComputer modelsModel interpretationQSARSARToxicity prediction

More Related Videos

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.6K
Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

878

Related Experiment Videos

Last Updated: Oct 3, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.1K
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.6K
Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

878

Area of Science:

  • Computational chemistry
  • Toxicology
  • Drug discovery

Background:

  • Chemical experimentation is increasingly being replaced by computational modeling and virtual experiments.
  • In silico methods leverage mathematics, chemistry, physics, and algorithms to predict properties from chemical structures.
  • These methods are crucial for understanding biological properties and toxicology.

Purpose of the Study:

  • To introduce the foundational principles of computational chemistry.
  • To explore the extension of computational methods to biological properties and toxicology modeling.
  • To highlight the role of in silico methods in chemical design and drug discovery.

Main Methods:

  • Utilizing in silico methods to mimic animal and wet experiments.
  • Employing quantitative structure-activity relationships (QSAR) models that consider the whole molecular structure.
  • Applying structure-activity relationship (SAR) models that analyze relevant substructures.

Main Results:

  • In silico methods allow for the prediction of biological properties and toxicological profiles.
  • QSAR and SAR models provide distinct approaches to property prediction based on chemical structure.
  • Virtual experiments confirm hypotheses and generate data for regulatory purposes.

Conclusions:

  • Computational chemistry and in silico modeling are essential tools in modern scientific research.
  • These methods accelerate chemical design, computational toxicology, and drug discovery processes.
  • Virtual experiments offer a powerful alternative to traditional experimentation, aiding in the development of new chemicals.