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

Data Validation01:15

Data Validation

3.4K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
3.4K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

442
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
442
Response Surface Methodology01:16

Response Surface Methodology

776
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:
776
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

897
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
897
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

318
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...
318
In Vitro Drug Release Testing: Overview, Development and Validation01:10

In Vitro Drug Release Testing: Overview, Development and Validation

476
In vitro dissolution and drug release tests assess how quickly and how much of a drug is released from its dosage form into an aqueous medium under standardized laboratory conditions. These tests are essential tools in pharmaceutical development and quality assurance, offering insight into the drug's performance before clinical use.During formulation development, dissolution testing identifies incomplete or inconsistent drug release issues. It also supports decisions on selecting the optimal...
476

You might also read

Related Articles

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

Sort by
Same author

Machine Learning Models with a Reject Option to Minimize Prediction Error: Application to Optical Properties of Dye Molecules.

Research square·2026
Same author

The ROBOKOP v1.0 knowledge graph system for exploring relationships between biomedical entities.

Scientific reports·2026
Same author

Binding Free Energies without Alchemy.

ArXiv·2026
Same author

CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2.

Journal of chemical information and modeling·2026
Same author

Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno-Associated Viral Vectors.

Biotechnology and bioengineering·2026
Same author

Medicines, Diseases, Indications, and Contraindications (MeDIC): a foundational resource to support drug repurposing.

Nucleic acids research·2025
Same journal

SpaceExpander: An Automated System for Drafting Markush Claims to Expand Chemical Space.

Molecular informatics·2026
Same journal

A Structure-Informed Atlas of Venom-Derived Peptides Reveals the Organization of Chemical Space.

Molecular informatics·2026
Same journal

ConGen: Targeted Molecule Generation Through Contrastive Learning and Latent Optimization.

Molecular informatics·2026
Same journal

Novel Molecules Generation Using Graph Generative Adversarial Networks.

Molecular informatics·2026
Same journal

An Attention-Driven Graph Transformer With Nonlinear Modeling and Neuro-Fuzzy Fusion for High-Order Toxic Molecular Graph Learning.

Molecular informatics·2026
Same journal

Molecular Modeling and Chemoinformatics in Ukraine.

Molecular informatics·2026
See all related articles

Related Experiment Video

Updated: Mar 17, 2026

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

2.6K

Best Practices for QSAR Model Development, Validation, and Exploitation.

Alexander Tropsha1

  • 1Laboratory for Molecular Modeling and Carolina, Center for Exploratory Cheminformatics Research, CB # 7568, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. alex_tropsha@unc.edu.

Molecular Informatics
|July 28, 2016
PubMed
Summary
This summary is machine-generated.

Quantitative Structure-Activity Relationship (QSAR) modeling offers best practices for analyzing chemical compounds. This review details QSAR workflows for rigorous, predictive models and successful virtual screening applications.

Keywords:
Drug discoveryModel validationQSAR modelingVirtual screening

More Related Videos

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

14.8K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.6K

Related Experiment Videos

Last Updated: Mar 17, 2026

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

2.6K
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

14.8K
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.6K

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Quantitative Structure-Activity Relationship (QSAR) modeling has matured over five decades.
  • QSAR is a key computational molecular modeling technique for analyzing chemical compounds.

Purpose of the Study:

  • To review best practices in QSAR modeling.
  • To present an integrative predictive QSAR workflow.
  • To highlight model validation and applicability domain definition.

Main Methods:

  • Data preparation: chemical structure and biological data curation.
  • Outlier detection and dataset balancing.
  • Internal and external model validation procedures.

Main Results:

  • Discussion of critical QSAR modeling routines and best practices.
  • Emphasis on achieving high statistical rigor and external predictive power.
  • Examples of successful QSAR applications in virtual screening.

Conclusions:

  • QSAR modeling provides robust protocols for exploring chemical compound collections.
  • Integrative workflows enhance model reliability and predictive capabilities.
  • Validated QSAR models are effective for identifying potential drug candidates via virtual screening.