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 Experiment Videos

Consensus QSAR models: do the benefits outweigh the complexity?

Mark Hewitt1, Mark T D Cronin, Judith C Madden

  • 1School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England.

Journal of Chemical Information and Modeling
|July 10, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

The Alarming Consequences of Workforce Reductions at the FDA, EPA, NIH and CDC in the United States.

NAM journal·2026
Same author

Development of Acute-to-Chronic Ratios (ACRs) to Support Ecotoxicity Prediction for Surfactants.

Environmental toxicology and chemistry·2026
Same author

MechoA+: A Chemical Structure Profiler Raising the Bar for the Prediction of Mechanisms of Toxic Action for Chemical Safety Assessment.

Environmental science & technology·2026
Same author

Organization-level determinants for low secondary traumatic stress in lay counselors delivering trauma-focused cognitive behavioral therapy in Kenya.

PLOS global public health·2026
Same author

Impact of social risk factors on TF-CBT engagement and strategies to mitigate the impact: A qualitative analysis.

PLOS mental health·2026
Same author

Organizational readiness for implementing infection control in European hospitals: insights from Coincidence Analysis.

Implementation science communications·2026
Same journal

Advancing Biochemical Molecule Registration, Representation and Search for New Drug Modalities.

Journal of chemical information and modeling·2026
Same journal

A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug-Hormone Receptor Interactions with Structure-Aware Validation.

Journal of chemical information and modeling·2026
Same journal

Intricate Role of Cholesterol in Membrane Fusion.

Journal of chemical information and modeling·2026
Same journal

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

Journal of chemical information and modeling·2026
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
See all related articles

Consensus regression models did not significantly improve quantitative structure-activity relationship (QSAR) predictions compared to single regression models. The increased complexity of consensus models is not warranted by the minimal statistical gains observed in this study.

Area of Science:

  • * Cheminformatics
  • * Computational Chemistry
  • * Predictive Modeling

Background:

  • * Quantitative structure-activity relationships (QSARs) are crucial for predicting chemical compound activity.
  • * Consensus regression models are proposed as an alternative to single regression models for improved QSAR development.
  • * Validation principles for QSARs, such as those from the OECD, guide model assessment.

Purpose of the Study:

  • * To compare the performance of consensus regression models against single multiple linear regression models for QSAR development.
  • * To evaluate the impact of model selection strategies (top models vs. diverse models) on consensus QSAR performance.
  • * To assess whether the increased complexity of consensus models is justified by statistical improvements.

Main Methods:

Related Experiment Videos

  • * Analysis of four diverse datasets: silastic membrane flux, phenol toxicity, fathead minnow toxicity, and flash point.
  • * Utilization of a genetic algorithm to generate a population of QSAR models.
  • * Development and comparison of two consensus models (top 10 models, maximally diverse models) against the best single model.

Main Results:

  • * Genetic algorithms effectively developed predictive QSAR models from large descriptor pools.
  • * Consensus regression models showed no significant improvement in statistical robustness or predictive power over single regression models.
  • * Consensus models derived from diverse QSARs were not superior to those derived from the best QSARs.

Conclusions:

  • * For the datasets studied, consensus regression models offer no significant advantage over single regression models.
  • * The increased complexity of consensus models is not justified by the minimal statistical improvements observed.
  • * The findings align with OECD principles, suggesting that simpler, statistically robust single models are preferable when consensus models provide marginal benefits.