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

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...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

You might also read

Related Articles

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

Sort by
Same author

Generation of Carbon and Phosphine Stereocenters via Cobaltaphotoredox-Catalyzed Enantioselective C-H Annulation.

Organic letters·2026
Same author

Metal-polyphenol nanomedicines for malignant tumor therapy.

Frontiers in chemistry·2026
Same author

GGAs: Regulation of Multiple Sorting Pathways and Potential Association With Human Diseases.

Journal of cellular and molecular medicine·2026
Same author

Gene cloning and expression analysis based on primary culture of fin cells from Centropyge vrolikii.

Journal of fish biology·2026
Same author

Targeting MEF2A suppresses microglial hyperactivation and synaptic phagocytosis to attenuate epilepsy pathogenesis.

Cell death & disease·2026
Same author

The senescent niche hypothesis: microglial dysfunction and replacement strategies in drug-resistant epilepsy.

Frontiers in immunology·2026

Related Experiment Video

Updated: Jun 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for

Jingheng Wu1, Juan Mei, Sixiang Wen

  • 1School of Chemistry and Chemical Engineering of Sun Yat-sen University, Guanzhou 510275, People's Republic of China.

Journal of Computational Chemistry
|June 1, 2010
PubMed
Summary
This summary is machine-generated.

A novel self-adaptive genetic algorithm-artificial neural network (GA-ANN) model improves quantitative structure-activity relationship (QSAR) predictions. This approach enhances model stability and predictive power compared to traditional methods.

Related Experiment Videos

Last Updated: Jun 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Computational Chemistry
  • Medicinal Chemistry
  • Bioinformatics

Background:

  • Quantitative structure-activity relationship (QSAR) models are crucial for drug discovery.
  • Artificial neural networks (ANNs) offer powerful modeling capabilities but can overfit.
  • Genetic algorithms (GAs) can optimize variable selection for complex models.

Purpose of the Study:

  • To develop a self-adaptive GA-ANN model for improved QSAR analysis.
  • To enhance the predictive accuracy and stability of QSAR models.
  • To compare the performance of the GA-ANN model against traditional methods like multiple linear regression (MLR).

Main Methods:

  • Utilized genetic algorithms (GAs) for optimal variable selection from molecular descriptors.
  • Employed artificial neural networks (ANNs) with a back-propagation training algorithm.
  • Implemented a self-adaptive GA-ANN approach with a novel estimation function to prevent overfitting.
  • Applied cross-validation techniques including leave-one-out, leave-multiple-out, Y-randomization, and external validation.

Main Results:

  • The self-adaptive GA-ANN model demonstrated superior performance in constructing ANN models.
  • Achieved higher cross-validation (CV) coefficient (Q(2)) and lower root mean square deviation (RMSD).
  • Validated models showed enhanced stability and predictive power compared to MLR-based QSAR studies.

Conclusions:

  • The self-adaptive GA-ANN model offers a significant advancement in QSAR methodology.
  • This approach effectively overcomes overfitting and improves predictive accuracy.
  • The GA-ANN framework presents a promising direction for developing robust QSAR models.