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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

360
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
360
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.9K
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...
1.9K

You might also read

Related Articles

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

Sort by
Same author

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same author

Exploring feature limitations in antimicrobial resistance prediction: machine learning and deep learning in A. baumannii.

Scientific reports·2026
Same author

CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.

Bioinformatics advances·2026
Same author

MT-ConBiFormer-GPT: multi-target molecular generation for low-data drug discovery via a contrastive BiFormer-GPT architecture and curriculum learning with cross-domain generalization.

Briefings in bioinformatics·2026
Same author

DeepDRP: Dose-response predictions of drug pairs using deep learning based on data-driven feature representation and dose-response curve characteristics.

PloS one·2026
Same author

ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing.

Scientific reports·2026

Related Experiment Video

Updated: Feb 21, 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

8.1K

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

Habib MotieGhader1, Sajjad Gharaghani2, Yosef Masoudi-Sobhanzadeh1

  • 1Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.

Iranian Journal of Pharmaceutical Research : IJPR
|October 6, 2017
PubMed
Summary

Two novel hybrid algorithms, Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), enhance Quantitative Structure-Activity Relationship (QSAR) feature selection. These methods offer superior predictive ability and faster convergence compared to existing algorithms.

Keywords:
Drug DesignFeature SelectionGenetic AlgorithmLearning AutomataQSAR

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

2.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K

Related Experiment Videos

Last Updated: Feb 21, 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

8.1K
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

2.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K

Area of Science:

  • Computational Chemistry
  • Bioinformatics
  • Machine Learning

Background:

  • Feature selection is critical for Quantitative Structure-Activity Relationship (QSAR) analysis.
  • Meta-heuristic algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are commonly used.
  • Existing methods face challenges in optimizing feature selection efficiency and convergence rates.

Purpose of the Study:

  • To propose two novel hybrid meta-heuristic algorithms for QSAR feature selection: Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA).
  • To evaluate the performance of SGALA and MGALA in selecting the minimum number of features from diverse datasets.
  • To compare the proposed algorithms against established methods in terms of outcome, predictive ability, and convergence rate.

Main Methods:

  • Development of SGALA, integrating GA and Learning Automata (LA) sequentially.
  • Development of MGALA, integrating GA and LA simultaneously.
  • Application of proposed algorithms to three distinct datasets for feature selection.
  • Comparative analysis with GA, PSO, ACO, and LA algorithms.
  • Validation using LS-Support Vector Regression (LS-SVR) models.

Main Results:

  • MGALA and SGALA demonstrated superior performance individually and on average compared to other feature selection algorithms.
  • The proposed algorithms exhibited a faster rate of convergence to optimal results than GA, ACO, PSO, and LA.
  • LS-SVR models utilizing features selected by SGALA and MGALA showed enhanced predictive ability.

Conclusions:

  • SGALA and MGALA are effective hybrid algorithms for QSAR feature selection.
  • The proposed methods offer improved predictive efficiency and faster convergence rates.
  • These algorithms represent a significant advancement in optimizing QSAR analysis through enhanced feature selection.